diff --git a/dev/.buildinfo b/dev/.buildinfo
index 086b5254..d0fd411c 100644
--- a/dev/.buildinfo
+++ b/dev/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0419996a16f2a3c75464ed74042897b9
+config: 0630c51683edc6ca083cc9b7428c67b6
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_images/sphx_glr_01_encodings_001.png b/dev/_images/sphx_glr_01_encodings_001.png
index 65d771a1..1aa55b3d 100644
Binary files a/dev/_images/sphx_glr_01_encodings_001.png and b/dev/_images/sphx_glr_01_encodings_001.png differ
diff --git a/dev/_images/sphx_glr_01_encodings_thumb.png b/dev/_images/sphx_glr_01_encodings_thumb.png
index 203ecae4..fa6cceb5 100644
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diff --git a/dev/_images/sphx_glr_08_join_aggregation_003.png b/dev/_images/sphx_glr_08_join_aggregation_003.png
index 36100ac6..b3dc3988 100644
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diff --git a/dev/_images/sphx_glr_09_interpolation_join_001.png b/dev/_images/sphx_glr_09_interpolation_join_001.png
index b7fb6a23..6360edce 100644
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diff --git a/dev/_images/sphx_glr_09_interpolation_join_002.png b/dev/_images/sphx_glr_09_interpolation_join_002.png
index 4b4d9870..cbab44ef 100644
Binary files a/dev/_images/sphx_glr_09_interpolation_join_002.png and b/dev/_images/sphx_glr_09_interpolation_join_002.png differ
diff --git a/dev/_images/sphx_glr_09_interpolation_join_003.png b/dev/_images/sphx_glr_09_interpolation_join_003.png
index 7d56525b..a5d928ef 100644
Binary files a/dev/_images/sphx_glr_09_interpolation_join_003.png and b/dev/_images/sphx_glr_09_interpolation_join_003.png differ
diff --git a/dev/_images/sphx_glr_09_interpolation_join_thumb.png b/dev/_images/sphx_glr_09_interpolation_join_thumb.png
index daed39ef..768d05f3 100644
Binary files a/dev/_images/sphx_glr_09_interpolation_join_thumb.png and b/dev/_images/sphx_glr_09_interpolation_join_thumb.png differ
diff --git a/dev/_sources/auto_examples/01_encodings.rst.txt b/dev/_sources/auto_examples/01_encodings.rst.txt
index a7acdb1a..c16be2ab 100644
--- a/dev/_sources/auto_examples/01_encodings.rst.txt
+++ b/dev/_sources/auto_examples/01_encodings.rst.txt
@@ -440,45 +440,44 @@ corresponding columns:
  .. code-block:: none
 
 
-    array(['station, state, estate', 'services, highway, service',
-           'investigative, investigations, criminal',
-           'nicholson, transit, taxicab',
-           'communications, division, applications', 'silver, spring, ride',
-           'training, building, recruit', 'gaithersburg, clarksburg, the',
-           'patrol, 4th, 6th', 'director, automated, office',
-           'programs, program, preparedness',
-           'rockville, twinbrook, downtown',
-           'eligibility, assistance, assisted', 'safety, section, collision',
-           'management, equipment, automotive', 'security, mc311, mccf',
+    array(['gaithersburg, clarksburg, the', 'supports, support, sports',
+           'district, patrol, 3rd', 'telephone, automated, stormwater',
+           'special, section, labor', 'environmental, regulatory, behavioral',
+           'welfare, childhood, child', 'maintenance, facilities, finance',
+           'services, highway, service', 'station, state, estate',
+           'traffic, safety, alcohol', 'security, custody, mcdc',
+           'silver, spring, urban', 'family, crimes, major',
+           'training, recruit, recruiting',
+           'technology, systems, information',
+           'building, construction, instruction',
+           'nicholson, transit, transport',
+           'communications, communication, division',
+           'emergency, centers, center', 'delivery, warehouse, liquor',
+           'management, mangement, engineering',
            'administration, administrative, battalion',
-           'behavioral, health, school', 'welfare, children, childhood',
-           'protective, procurement, project',
-           'development, government, stormwater', 'supports, support, sports',
-           'family, animal, robbery', 'emergency, commuter, duplicating',
-           'custody, toddlers, mcdc', 'district, urban, 3rd',
-           'technology, systems, telephone', 'council, centers, members',
-           'delivery, special, operations',
-           'maintenance, facilities, finance', 'captain, chief, autobody',
-           'liquor, clerk, store', 'officer, office, police',
-           'master, registered, water', 'operator, bus, operations',
-           'administrative, legislative, principal',
-           'technician, mechanic, supply', 'manager, budget, engineer',
-           'recreation, renovation, resource', 'school, room, behavioral',
-           'coordinator, transit, coordinating',
-           'enforcement, permitting, inspector',
-           'information, technology, technologist',
-           'assistance, income, client', 'therapist, sheriff, plumber',
+           'investigative, explosive, investigations',
+           'eligibility, assistance, assisted', 'health, school, based',
+           'fleet, animal, bureau', 'downtown, rockville, library',
+           'accounts, toddlers, council', 'protective, programs, program',
+           'officer, office, traffic', 'warehouse, welfare, caseworker',
            'firefighter, rescuer, recruit',
-           'correctional, correction, regional',
-           'accountant, assistant, library',
+           'librarian, candidate, psychiatric', 'income, assistance, client',
+           'coordinator, services, service', 'manager, iii, management',
+           'equipment, investment, investigator', 'operator, bus, operations',
+           'specialist, special, quality',
+           'enforcement, inspector, permitting',
+           'technician, mechanic, supply',
            'communications, telecommunications, safety',
-           'services, service, aide', 'community, health, nurse',
-           'sergeant, cadet, emergency', 'craftsworker, worker, social',
-           'specialist, special, procurement',
-           'crossing, purchasing, planning', 'warehouse, welfare, driver',
-           'corporal, erp, behavioral', 'program, programs, projects',
-           'equipment, investment, investigator',
-           'lieutenant, attendant, shift'], dtype=object)
+           'crossing, parking, guard',
+           'administrative, legislative, principal',
+           'correctional, correction, corporal', 'school, room, behavioral',
+           'community, nurse, health', 'liquor, clerk, store',
+           'lieutenant, maintenance, client', 'sheriff, deputy, aide',
+           'accountant, assistant, library', 'sergeant, police, cadet',
+           'captain, chief, autobody', 'supervisor, supervisory, transit',
+           'program, programs, projects', 'environmental, therapist, budget',
+           'master, registered, meter', 'information, technology, renovation',
+           'planning, senior, background'], dtype=object)
 
 
 
@@ -560,7 +559,7 @@ Let's look at the cross-validated R2 score of our model:
 
  .. code-block:: none
 
-    R2 score:  mean: 0.921; std: 0.016
+    R2 score:  mean: 0.923; std: 0.013
 
 
 
@@ -696,7 +695,7 @@ to plot the feature importances.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 19.568 seconds)
+   **Total running time of the script:** (1 minutes 8.670 seconds)
 
 
 .. _sphx_glr_download_auto_examples_01_encodings.py:
diff --git a/dev/_sources/auto_examples/02_feature_interpretation_with_gapencoder.rst.txt b/dev/_sources/auto_examples/02_feature_interpretation_with_gapencoder.rst.txt
index 694892af..36c38497 100644
--- a/dev/_sources/auto_examples/02_feature_interpretation_with_gapencoder.rst.txt
+++ b/dev/_sources/auto_examples/02_feature_interpretation_with_gapencoder.rst.txt
@@ -504,7 +504,7 @@ as a set of latent topics.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.041 seconds)
+   **Total running time of the script:** (0 minutes 1.648 seconds)
 
 
 .. _sphx_glr_download_auto_examples_02_feature_interpretation_with_gapencoder.py:
diff --git a/dev/_sources/auto_examples/03_datetime_encoder.rst.txt b/dev/_sources/auto_examples/03_datetime_encoder.rst.txt
index d7276e52..9707ebad 100644
--- a/dev/_sources/auto_examples/03_datetime_encoder.rst.txt
+++ b/dev/_sources/auto_examples/03_datetime_encoder.rst.txt
@@ -610,7 +610,7 @@ and transforms datetime columns by default.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.948 seconds)
+   **Total running time of the script:** (0 minutes 4.149 seconds)
 
 
 .. _sphx_glr_download_auto_examples_03_datetime_encoder.py:
diff --git a/dev/_sources/auto_examples/04_fuzzy_joining.rst.txt b/dev/_sources/auto_examples/04_fuzzy_joining.rst.txt
index dab97550..142db368 100644
--- a/dev/_sources/auto_examples/04_fuzzy_joining.rst.txt
+++ b/dev/_sources/auto_examples/04_fuzzy_joining.rst.txt
@@ -1711,7 +1711,7 @@ introduced into a grid search:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 19.674 seconds)
+   **Total running time of the script:** (0 minutes 17.135 seconds)
 
 
 .. _sphx_glr_download_auto_examples_04_fuzzy_joining.py:
diff --git a/dev/_sources/auto_examples/05_deduplication.rst.txt b/dev/_sources/auto_examples/05_deduplication.rst.txt
index 91a2ccc9..6b32cfbd 100644
--- a/dev/_sources/auto_examples/05_deduplication.rst.txt
+++ b/dev/_sources/auto_examples/05_deduplication.rst.txt
@@ -335,7 +335,7 @@ or |MinHash|.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.680 seconds)
+   **Total running time of the script:** (0 minutes 4.909 seconds)
 
 
 .. _sphx_glr_download_auto_examples_05_deduplication.py:
diff --git a/dev/_sources/auto_examples/06_ken_embeddings.rst.txt b/dev/_sources/auto_examples/06_ken_embeddings.rst.txt
index 9ff2206f..a326a489 100644
--- a/dev/_sources/auto_examples/06_ken_embeddings.rst.txt
+++ b/dev/_sources/auto_examples/06_ken_embeddings.rst.txt
@@ -305,7 +305,7 @@ We will start by checking out the available tables with
  .. code-block:: none
 
 
-    {'companies', 'games', 'all_entities', 'albums', 'schools', 'movies'}
+    {'all_entities', 'companies', 'movies', 'albums', 'games', 'schools'}
 
 
 
@@ -840,7 +840,7 @@ It helped significantly improve the prediction score.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (11 minutes 7.838 seconds)
+   **Total running time of the script:** (10 minutes 30.856 seconds)
 
 
 .. _sphx_glr_download_auto_examples_06_ken_embeddings.py:
diff --git a/dev/_sources/auto_examples/07_multiple_key_join.rst.txt b/dev/_sources/auto_examples/07_multiple_key_join.rst.txt
index 71dacf7c..4897f89a 100644
--- a/dev/_sources/auto_examples/07_multiple_key_join.rst.txt
+++ b/dev/_sources/auto_examples/07_multiple_key_join.rst.txt
@@ -1226,7 +1226,7 @@ The results:
     /home/circleci/project/miniconda/envs/testenv/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:228: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros
       warnings.warn(
 
-    0.58995
+    0.5878500000000001
 
 
 
@@ -1244,7 +1244,7 @@ Our final cross-validated accuracy score is 0.58.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (11 minutes 29.384 seconds)
+   **Total running time of the script:** (10 minutes 56.053 seconds)
 
 
 .. _sphx_glr_download_auto_examples_07_multiple_key_join.py:
diff --git a/dev/_sources/auto_examples/08_join_aggregation.rst.txt b/dev/_sources/auto_examples/08_join_aggregation.rst.txt
index c66ab1fe..8ba3cd56 100644
--- a/dev/_sources/auto_examples/08_join_aggregation.rst.txt
+++ b/dev/_sources/auto_examples/08_join_aggregation.rst.txt
@@ -844,75 +844,75 @@ operation maximizing our validation score.
         </tr>
         <tr>
           <th>split1_test_score</th>
-          <td>0.047956</td>
-          <td>0.063122</td>
-          <td>0.082469</td>
-          <td>0.057066</td>
-          <td>0.062993</td>
+          <td>0.039475</td>
+          <td>0.047479</td>
+          <td>0.075629</td>
+          <td>0.065258</td>
+          <td>0.070249</td>
         </tr>
         <tr>
           <th>split2_test_score</th>
-          <td>0.077542</td>
-          <td>0.081570</td>
-          <td>0.094021</td>
-          <td>0.098690</td>
-          <td>0.097006</td>
+          <td>0.075227</td>
+          <td>0.083063</td>
+          <td>0.092384</td>
+          <td>0.096931</td>
+          <td>0.089502</td>
         </tr>
         <tr>
           <th>split3_test_score</th>
-          <td>0.043531</td>
-          <td>0.065571</td>
-          <td>0.048403</td>
-          <td>0.065745</td>
-          <td>0.069058</td>
+          <td>0.042883</td>
+          <td>0.060734</td>
+          <td>0.061950</td>
+          <td>0.070185</td>
+          <td>0.077840</td>
         </tr>
         <tr>
           <th>split4_test_score</th>
-          <td>0.146376</td>
-          <td>0.134605</td>
-          <td>0.134260</td>
-          <td>0.144846</td>
-          <td>0.146525</td>
+          <td>0.140699</td>
+          <td>0.133569</td>
+          <td>0.139557</td>
+          <td>0.152427</td>
+          <td>0.143822</td>
         </tr>
         <tr>
           <th>split5_test_score</th>
-          <td>0.106774</td>
-          <td>0.110818</td>
-          <td>0.108646</td>
-          <td>0.103209</td>
-          <td>0.118163</td>
+          <td>0.113387</td>
+          <td>0.114269</td>
+          <td>0.109349</td>
+          <td>0.106754</td>
+          <td>0.116180</td>
         </tr>
         <tr>
           <th>split6_test_score</th>
-          <td>0.087637</td>
-          <td>0.101564</td>
-          <td>0.106001</td>
-          <td>0.108448</td>
-          <td>0.104643</td>
+          <td>0.080712</td>
+          <td>0.095461</td>
+          <td>0.107454</td>
+          <td>0.107903</td>
+          <td>0.108229</td>
         </tr>
         <tr>
           <th>split7_test_score</th>
-          <td>0.069828</td>
-          <td>0.061365</td>
-          <td>0.063359</td>
-          <td>0.078356</td>
-          <td>0.087845</td>
+          <td>0.070899</td>
+          <td>0.067751</td>
+          <td>0.059840</td>
+          <td>0.069572</td>
+          <td>0.078243</td>
         </tr>
         <tr>
           <th>split8_test_score</th>
-          <td>0.103975</td>
-          <td>0.113042</td>
-          <td>0.124293</td>
-          <td>0.122794</td>
-          <td>0.134883</td>
+          <td>0.103105</td>
+          <td>0.112480</td>
+          <td>0.121120</td>
+          <td>0.124410</td>
+          <td>0.127232</td>
         </tr>
         <tr>
           <th>split9_test_score</th>
-          <td>0.119554</td>
-          <td>0.151334</td>
-          <td>0.162009</td>
-          <td>0.169056</td>
-          <td>0.191246</td>
+          <td>0.107526</td>
+          <td>0.157958</td>
+          <td>0.171831</td>
+          <td>0.164579</td>
+          <td>0.184743</td>
         </tr>
       </tbody>
     </table>
@@ -1003,7 +1003,7 @@ exhaustive histogram over all the possible values of ratings
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 18.997 seconds)
+   **Total running time of the script:** (0 minutes 21.351 seconds)
 
 
 .. _sphx_glr_download_auto_examples_08_join_aggregation.py:
diff --git a/dev/_sources/auto_examples/09_interpolation_join.rst.txt b/dev/_sources/auto_examples/09_interpolation_join.rst.txt
index aca2dea8..bbdf0958 100644
--- a/dev/_sources/auto_examples/09_interpolation_join.rst.txt
+++ b/dev/_sources/auto_examples/09_interpolation_join.rst.txt
@@ -350,9 +350,9 @@ To avoid clashes in the column names, we use the ``suffix`` parameter to append
           <td>42.6</td>
           <td>0.0</td>
           <td>NaN</td>
-          <td>34.050713</td>
-          <td>79.557726</td>
-          <td>0.061708</td>
+          <td>33.273594</td>
+          <td>93.598452</td>
+          <td>-0.143711</td>
         </tr>
         <tr>
           <th>1</th>
@@ -362,9 +362,9 @@ To avoid clashes in the column names, we use the ``suffix`` parameter to append
           <td>26.9</td>
           <td>NaN</td>
           <td>NaN</td>
-          <td>26.447479</td>
-          <td>80.855013</td>
-          <td>0.282900</td>
+          <td>27.421386</td>
+          <td>145.126263</td>
+          <td>0.032536</td>
         </tr>
         <tr>
           <th>2</th>
@@ -374,9 +374,9 @@ To avoid clashes in the column names, we use the ``suffix`` parameter to append
           <td>41.6</td>
           <td>NaN</td>
           <td>NaN</td>
-          <td>31.378841</td>
-          <td>29.758804</td>
-          <td>0.061708</td>
+          <td>31.697874</td>
+          <td>29.228029</td>
+          <td>-0.146347</td>
         </tr>
         <tr>
           <th>3</th>
@@ -386,9 +386,9 @@ To avoid clashes in the column names, we use the ``suffix`` parameter to append
           <td>46.6</td>
           <td>0.0</td>
           <td>NaN</td>
-          <td>33.341582</td>
-          <td>56.433833</td>
-          <td>0.061708</td>
+          <td>33.469607</td>
+          <td>55.614446</td>
+          <td>-0.146347</td>
         </tr>
         <tr>
           <th>4</th>
@@ -398,9 +398,9 @@ To avoid clashes in the column names, we use the ``suffix`` parameter to append
           <td>36.1</td>
           <td>0.0</td>
           <td>NaN</td>
-          <td>29.703763</td>
-          <td>34.348435</td>
-          <td>0.061708</td>
+          <td>29.672556</td>
+          <td>28.401216</td>
+          <td>0.129800</td>
         </tr>
       </tbody>
     </table>
@@ -580,7 +580,7 @@ Then, we use it to transform the flights table -- it adds a ``'TMAX'`` column co
           <td>MI</td>
           <td>42.212059</td>
           <td>-83.348836</td>
-          <td>1.573918</td>
+          <td>0.100304</td>
         </tr>
         <tr>
           <th>1</th>
@@ -592,7 +592,7 @@ Then, we use it to transform the flights table -- it adds a ``'TMAX'`` column co
           <td>MI</td>
           <td>42.212059</td>
           <td>-83.348836</td>
-          <td>16.273818</td>
+          <td>15.822712</td>
         </tr>
         <tr>
           <th>2</th>
@@ -604,7 +604,7 @@ Then, we use it to transform the flights table -- it adds a ``'TMAX'`` column co
           <td>MI</td>
           <td>42.212059</td>
           <td>-83.348836</td>
-          <td>5.417276</td>
+          <td>5.827000</td>
         </tr>
         <tr>
           <th>3</th>
@@ -616,7 +616,7 @@ Then, we use it to transform the flights table -- it adds a ``'TMAX'`` column co
           <td>MI</td>
           <td>42.212059</td>
           <td>-83.348836</td>
-          <td>6.667582</td>
+          <td>6.568854</td>
         </tr>
         <tr>
           <th>4</th>
@@ -628,7 +628,7 @@ Then, we use it to transform the flights table -- it adds a ``'TMAX'`` column co
           <td>MI</td>
           <td>42.212059</td>
           <td>-83.348836</td>
-          <td>6.430813</td>
+          <td>7.027463</td>
         </tr>
       </tbody>
     </table>
@@ -678,11 +678,11 @@ States with the lowest average predicted temperatures: Alaska, Montana, North Da
 
 
     state
-    AK   -4.848166
-    WA   -0.189643
-    MT    0.126340
-    ND    1.035899
-    MN    1.665222
+    AK   -4.202223
+    MT    0.497644
+    WA    0.735437
+    MN    1.459978
+    ND    1.460721
     Name: TMAX, dtype: float64
 
 
@@ -707,11 +707,11 @@ States with the highest predicted temperatures: Puerto Rico, Virgin Islands, Haw
 
 
     state
-    LA    21.833117
-    FL    24.515461
-    HI    26.971245
-    VI    30.231336
-    PR    30.808106
+    LA    21.635930
+    FL    24.662567
+    HI    27.179546
+    VI    29.895677
+    PR    30.640109
     Name: TMAX, dtype: float64
 
 
@@ -787,7 +787,7 @@ It is a generalization of the :func:`~skrub.fuzzy_join`, as :func:`~skrub.fuzzy_
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.503 seconds)
+   **Total running time of the script:** (0 minutes 6.568 seconds)
 
 
 .. _sphx_glr_download_auto_examples_09_interpolation_join.py:
diff --git a/dev/_sources/auto_examples/sg_execution_times.rst.txt b/dev/_sources/auto_examples/sg_execution_times.rst.txt
index 9fe52d1c..777a2cb2 100644
--- a/dev/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/dev/_sources/auto_examples/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**24:52.633** total execution time for 9 files **from auto_examples**:
+**23:31.339** total execution time for 9 files **from auto_examples**:
 
 .. container::
 
@@ -33,29 +33,29 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_07_multiple_key_join.py` (``07_multiple_key_join.py``)
-     - 11:29.384
+     - 10:56.053
      - 0.0
    * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``06_ken_embeddings.py``)
-     - 11:07.838
+     - 10:30.856
      - 0.0
    * - :ref:`sphx_glr_auto_examples_01_encodings.py` (``01_encodings.py``)
-     - 01:19.568
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``04_fuzzy_joining.py``)
-     - 00:19.674
+     - 01:08.670
      - 0.0
    * - :ref:`sphx_glr_auto_examples_08_join_aggregation.py` (``08_join_aggregation.py``)
-     - 00:18.997
+     - 00:21.351
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``09_interpolation_join.py``)
-     - 00:05.503
+   * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``04_fuzzy_joining.py``)
+     - 00:17.135
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``03_datetime_encoder.py``)
-     - 00:04.948
+   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``09_interpolation_join.py``)
+     - 00:06.568
      - 0.0
    * - :ref:`sphx_glr_auto_examples_05_deduplication.py` (``05_deduplication.py``)
-     - 00:04.680
+     - 00:04.909
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``03_datetime_encoder.py``)
+     - 00:04.149
      - 0.0
    * - :ref:`sphx_glr_auto_examples_02_feature_interpretation_with_gapencoder.py` (``02_feature_interpretation_with_gapencoder.py``)
-     - 00:02.041
+     - 00:01.648
      - 0.0
diff --git a/dev/_sources/install.rst.txt b/dev/_sources/install.rst.txt
index bfa3114f..cf572c4f 100644
--- a/dev/_sources/install.rst.txt
+++ b/dev/_sources/install.rst.txt
@@ -34,6 +34,7 @@ Installing
     </div>
           <div class="tab-pane fade" id="conda-tab-pane" role="tabpanel" aria-labelledby="conda-tab" tabindex="0">
             <hr />
+    <strong>This will not work yet!</strong> The package is still waiting to be published on conda-forge. You can follow progress <a href="https://github.com/conda-forge/staged-recipes/pull/24766">here</a>.
 
 .. code:: console
 
@@ -44,6 +45,7 @@ Installing
         </div>
         <div class="tab-pane fade" id="mamba-tab-pane" role="tabpanel" aria-labelledby="mamba-tab" tabindex="0">
             <hr />
+        <strong>This will not work yet!</strong> The package is still waiting to be published on conda-forge. You can follow progress <a href="https://github.com/conda-forge/staged-recipes/pull/24766">here</a>.
 
 .. code:: console
 
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index 95e4edfb..00e5a2f1 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**24:52.633** total execution time for 9 files **from all galleries**:
+**23:31.339** total execution time for 9 files **from all galleries**:
 
 .. container::
 
@@ -33,29 +33,29 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_07_multiple_key_join.py` (``../examples/07_multiple_key_join.py``)
-     - 11:29.384
+     - 10:56.053
      - 0.0
    * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``../examples/06_ken_embeddings.py``)
-     - 11:07.838
+     - 10:30.856
      - 0.0
    * - :ref:`sphx_glr_auto_examples_01_encodings.py` (``../examples/01_encodings.py``)
-     - 01:19.568
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``../examples/04_fuzzy_joining.py``)
-     - 00:19.674
+     - 01:08.670
      - 0.0
    * - :ref:`sphx_glr_auto_examples_08_join_aggregation.py` (``../examples/08_join_aggregation.py``)
-     - 00:18.997
+     - 00:21.351
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``../examples/09_interpolation_join.py``)
-     - 00:05.503
+   * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``../examples/04_fuzzy_joining.py``)
+     - 00:17.135
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``../examples/03_datetime_encoder.py``)
-     - 00:04.948
+   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``../examples/09_interpolation_join.py``)
+     - 00:06.568
      - 0.0
    * - :ref:`sphx_glr_auto_examples_05_deduplication.py` (``../examples/05_deduplication.py``)
-     - 00:04.680
+     - 00:04.909
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``../examples/03_datetime_encoder.py``)
+     - 00:04.149
      - 0.0
    * - :ref:`sphx_glr_auto_examples_02_feature_interpretation_with_gapencoder.py` (``../examples/02_feature_interpretation_with_gapencoder.py``)
-     - 00:02.041
+     - 00:01.648
      - 0.0
diff --git a/dev/auto_examples/01_encodings.html b/dev/auto_examples/01_encodings.html
index ee77d716..f9b1fe99 100644
--- a/dev/auto_examples/01_encodings.html
+++ b/dev/auto_examples/01_encodings.html
@@ -797,45 +797,44 @@ <h2>A simple prediction pipeline<a class="headerlink" href="#a-simple-prediction
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../generated/skrub.TableVectorizer.html#skrub.TableVectorizer.named_transformers_" title="skrub.TableVectorizer.named_transformers_" class="sphx-glr-backref-module-skrub sphx-glr-backref-type-py-property"><span class="n">tv</span><span class="o">.</span><span class="n">named_transformers_</span></a><span class="p">[</span><span class="s2">&quot;high_cardinality&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([&#39;station, state, estate&#39;, &#39;services, highway, service&#39;,
-       &#39;investigative, investigations, criminal&#39;,
-       &#39;nicholson, transit, taxicab&#39;,
-       &#39;communications, division, applications&#39;, &#39;silver, spring, ride&#39;,
-       &#39;training, building, recruit&#39;, &#39;gaithersburg, clarksburg, the&#39;,
-       &#39;patrol, 4th, 6th&#39;, &#39;director, automated, office&#39;,
-       &#39;programs, program, preparedness&#39;,
-       &#39;rockville, twinbrook, downtown&#39;,
-       &#39;eligibility, assistance, assisted&#39;, &#39;safety, section, collision&#39;,
-       &#39;management, equipment, automotive&#39;, &#39;security, mc311, mccf&#39;,
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([&#39;gaithersburg, clarksburg, the&#39;, &#39;supports, support, sports&#39;,
+       &#39;district, patrol, 3rd&#39;, &#39;telephone, automated, stormwater&#39;,
+       &#39;special, section, labor&#39;, &#39;environmental, regulatory, behavioral&#39;,
+       &#39;welfare, childhood, child&#39;, &#39;maintenance, facilities, finance&#39;,
+       &#39;services, highway, service&#39;, &#39;station, state, estate&#39;,
+       &#39;traffic, safety, alcohol&#39;, &#39;security, custody, mcdc&#39;,
+       &#39;silver, spring, urban&#39;, &#39;family, crimes, major&#39;,
+       &#39;training, recruit, recruiting&#39;,
+       &#39;technology, systems, information&#39;,
+       &#39;building, construction, instruction&#39;,
+       &#39;nicholson, transit, transport&#39;,
+       &#39;communications, communication, division&#39;,
+       &#39;emergency, centers, center&#39;, &#39;delivery, warehouse, liquor&#39;,
+       &#39;management, mangement, engineering&#39;,
        &#39;administration, administrative, battalion&#39;,
-       &#39;behavioral, health, school&#39;, &#39;welfare, children, childhood&#39;,
-       &#39;protective, procurement, project&#39;,
-       &#39;development, government, stormwater&#39;, &#39;supports, support, sports&#39;,
-       &#39;family, animal, robbery&#39;, &#39;emergency, commuter, duplicating&#39;,
-       &#39;custody, toddlers, mcdc&#39;, &#39;district, urban, 3rd&#39;,
-       &#39;technology, systems, telephone&#39;, &#39;council, centers, members&#39;,
-       &#39;delivery, special, operations&#39;,
-       &#39;maintenance, facilities, finance&#39;, &#39;captain, chief, autobody&#39;,
-       &#39;liquor, clerk, store&#39;, &#39;officer, office, police&#39;,
-       &#39;master, registered, water&#39;, &#39;operator, bus, operations&#39;,
-       &#39;administrative, legislative, principal&#39;,
-       &#39;technician, mechanic, supply&#39;, &#39;manager, budget, engineer&#39;,
-       &#39;recreation, renovation, resource&#39;, &#39;school, room, behavioral&#39;,
-       &#39;coordinator, transit, coordinating&#39;,
-       &#39;enforcement, permitting, inspector&#39;,
-       &#39;information, technology, technologist&#39;,
-       &#39;assistance, income, client&#39;, &#39;therapist, sheriff, plumber&#39;,
+       &#39;investigative, explosive, investigations&#39;,
+       &#39;eligibility, assistance, assisted&#39;, &#39;health, school, based&#39;,
+       &#39;fleet, animal, bureau&#39;, &#39;downtown, rockville, library&#39;,
+       &#39;accounts, toddlers, council&#39;, &#39;protective, programs, program&#39;,
+       &#39;officer, office, traffic&#39;, &#39;warehouse, welfare, caseworker&#39;,
        &#39;firefighter, rescuer, recruit&#39;,
-       &#39;correctional, correction, regional&#39;,
-       &#39;accountant, assistant, library&#39;,
+       &#39;librarian, candidate, psychiatric&#39;, &#39;income, assistance, client&#39;,
+       &#39;coordinator, services, service&#39;, &#39;manager, iii, management&#39;,
+       &#39;equipment, investment, investigator&#39;, &#39;operator, bus, operations&#39;,
+       &#39;specialist, special, quality&#39;,
+       &#39;enforcement, inspector, permitting&#39;,
+       &#39;technician, mechanic, supply&#39;,
        &#39;communications, telecommunications, safety&#39;,
-       &#39;services, service, aide&#39;, &#39;community, health, nurse&#39;,
-       &#39;sergeant, cadet, emergency&#39;, &#39;craftsworker, worker, social&#39;,
-       &#39;specialist, special, procurement&#39;,
-       &#39;crossing, purchasing, planning&#39;, &#39;warehouse, welfare, driver&#39;,
-       &#39;corporal, erp, behavioral&#39;, &#39;program, programs, projects&#39;,
-       &#39;equipment, investment, investigator&#39;,
-       &#39;lieutenant, attendant, shift&#39;], dtype=object)
+       &#39;crossing, parking, guard&#39;,
+       &#39;administrative, legislative, principal&#39;,
+       &#39;correctional, correction, corporal&#39;, &#39;school, room, behavioral&#39;,
+       &#39;community, nurse, health&#39;, &#39;liquor, clerk, store&#39;,
+       &#39;lieutenant, maintenance, client&#39;, &#39;sheriff, deputy, aide&#39;,
+       &#39;accountant, assistant, library&#39;, &#39;sergeant, police, cadet&#39;,
+       &#39;captain, chief, autobody&#39;, &#39;supervisor, supervisory, transit&#39;,
+       &#39;program, programs, projects&#39;, &#39;environmental, therapist, budget&#39;,
+       &#39;master, registered, meter&#39;, &#39;information, technology, renovation&#39;,
+       &#39;planning, senior, background&#39;], dtype=object)
 </pre></div>
 </div>
 <ul class="simple">
@@ -868,7 +867,7 @@ <h2>A simple prediction pipeline<a class="headerlink" href="#a-simple-prediction
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;R2 score:  mean: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scores</span></a><span class="p">)</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">; std: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scores</span></a><span class="p">)</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>R2 score:  mean: 0.921; std: 0.016
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>R2 score:  mean: 0.923; std: 0.013
 </pre></div>
 </div>
 <p>The simple pipeline applied on this complex dataset gave us very good results.</p>
@@ -937,7 +936,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 <p>We saw that by default, it works well on a heterogeneous dataset.</p>
 <p>To better understand our dataset, and without much effort, we were also able
 to plot the feature importances.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 19.568 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 8.670 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-01-encodings-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/01_encodings.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/02_feature_interpretation_with_gapencoder.html b/dev/auto_examples/02_feature_interpretation_with_gapencoder.html
index 45a3d6b7..ef8de282 100644
--- a/dev/auto_examples/02_feature_interpretation_with_gapencoder.html
+++ b/dev/auto_examples/02_feature_interpretation_with_gapencoder.html
@@ -850,7 +850,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 <p>In this notebook, we have seen how to interpret the output of the
 <a class="reference internal" href="../generated/skrub.GapEncoder.html#skrub.GapEncoder" title="skrub.GapEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">GapEncoder</span></code></a>, and how it can be used to summarize categorical variables
 as a set of latent topics.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.041 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.648 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-02-feature-interpretation-with-gapencoder-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/02_feature_interpretation_with_gapencoder.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/03_datetime_encoder.html b/dev/auto_examples/03_datetime_encoder.html
index b3395352..153bcbd7 100644
--- a/dev/auto_examples/03_datetime_encoder.html
+++ b/dev/auto_examples/03_datetime_encoder.html
@@ -850,7 +850,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 features from a date column.
 Also check out the <a class="reference internal" href="../generated/skrub.TableVectorizer.html#skrub.TableVectorizer" title="skrub.TableVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">TableVectorizer</span></code></a>, which automatically recognizes
 and transforms datetime columns by default.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.948 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.149 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-03-datetime-encoder-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/03_datetime_encoder.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/04_fuzzy_joining.html b/dev/auto_examples/04_fuzzy_joining.html
index 627ab600..bd977be5 100644
--- a/dev/auto_examples/04_fuzzy_joining.html
+++ b/dev/auto_examples/04_fuzzy_joining.html
@@ -1781,7 +1781,7 @@ <h2>Using the <a class="reference internal" href="../generated/skrub.Joiner.html
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best parameters: {&#39;joiner-1__max_dist&#39;: 0.1, &#39;joiner-2__max_dist&#39;: 0.9, &#39;joiner-3__max_dist&#39;: 0.1}
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 19.674 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.135 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-04-fuzzy-joining-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/04_fuzzy_joining.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/05_deduplication.html b/dev/auto_examples/05_deduplication.html
index b77b38a9..c560e455 100644
--- a/dev/auto_examples/05_deduplication.html
+++ b/dev/auto_examples/05_deduplication.html
@@ -665,7 +665,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 carry useful information for our machine learning task.
 Otherwise, we prefer using encoding methods such as <a class="reference internal" href="../generated/skrub.GapEncoder.html#skrub.GapEncoder" title="skrub.GapEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">GapEncoder</span></code></a>
 or <a class="reference internal" href="../generated/skrub.MinHashEncoder.html#skrub.MinHashEncoder" title="skrub.MinHashEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinHashEncoder</span></code></a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.680 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.909 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-05-deduplication-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/05_deduplication.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/06_ken_embeddings.html b/dev/auto_examples/06_ken_embeddings.html
index 3e05ee4e..bc7b2d01 100644
--- a/dev/auto_examples/06_ken_embeddings.html
+++ b/dev/auto_examples/06_ken_embeddings.html
@@ -672,7 +672,7 @@ <h2>Extracting entity embeddings<a class="headerlink" href="#extracting-entity-e
 <a href="../generated/skrub.datasets.fetch_ken_table_aliases.html#skrub.datasets.fetch_ken_table_aliases" title="skrub.datasets.fetch_ken_table_aliases" class="sphx-glr-backref-module-skrub-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_ken_table_aliases</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;companies&#39;, &#39;games&#39;, &#39;all_entities&#39;, &#39;albums&#39;, &#39;schools&#39;, &#39;movies&#39;}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;all_entities&#39;, &#39;companies&#39;, &#39;movies&#39;, &#39;albums&#39;, &#39;games&#39;, &#39;schools&#39;}
 </pre></div>
 </div>
 <p>The <em>games</em> table is the most relevant to our case.
@@ -968,7 +968,7 @@ <h3>Plotting the results<a class="headerlink" href="#plotting-the-results" title
 additional background information on the game and the publisher of the
 game that would otherwise be missed.</p>
 <p>It helped significantly improve the prediction score.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (11 minutes 7.838 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (10 minutes 30.856 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-06-ken-embeddings-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/06_ken_embeddings.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/07_multiple_key_join.html b/dev/auto_examples/07_multiple_key_join.html
index 8ca92bac..f8865069 100644
--- a/dev/auto_examples/07_multiple_key_join.html
+++ b/dev/auto_examples/07_multiple_key_join.html
@@ -1514,7 +1514,7 @@ <h2>Joining: feature augmentation across tables<a class="headerlink" href="#join
 /home/circleci/project/miniconda/envs/testenv/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:228: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros
   warnings.warn(
 
-0.58995
+0.5878500000000001
 </pre></div>
 </div>
 </section>
@@ -1524,7 +1524,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 on imprecise and multiple-key correspondences.
 This is made easy by skrub’s <a class="reference internal" href="../generated/skrub.Joiner.html#skrub.Joiner" title="skrub.Joiner"><code class="xref py py-func docutils literal notranslate"><span class="pre">Joiner()</span></code></a> transformer.</p>
 <p>Our final cross-validated accuracy score is 0.58.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (11 minutes 29.384 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (10 minutes 56.053 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-07-multiple-key-join-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/07_multiple_key_join.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/08_join_aggregation.html b/dev/auto_examples/08_join_aggregation.html
index 3d107abd..42442889 100644
--- a/dev/auto_examples/08_join_aggregation.html
+++ b/dev/auto_examples/08_join_aggregation.html
@@ -1096,75 +1096,75 @@ <h2>Hyper-parameters tuning and cross validation<a class="headerlink" href="#hyp
     </tr>
     <tr>
       <th>split1_test_score</th>
-      <td>0.047956</td>
-      <td>0.063122</td>
-      <td>0.082469</td>
-      <td>0.057066</td>
-      <td>0.062993</td>
+      <td>0.039475</td>
+      <td>0.047479</td>
+      <td>0.075629</td>
+      <td>0.065258</td>
+      <td>0.070249</td>
     </tr>
     <tr>
       <th>split2_test_score</th>
-      <td>0.077542</td>
-      <td>0.081570</td>
-      <td>0.094021</td>
-      <td>0.098690</td>
-      <td>0.097006</td>
+      <td>0.075227</td>
+      <td>0.083063</td>
+      <td>0.092384</td>
+      <td>0.096931</td>
+      <td>0.089502</td>
     </tr>
     <tr>
       <th>split3_test_score</th>
-      <td>0.043531</td>
-      <td>0.065571</td>
-      <td>0.048403</td>
-      <td>0.065745</td>
-      <td>0.069058</td>
+      <td>0.042883</td>
+      <td>0.060734</td>
+      <td>0.061950</td>
+      <td>0.070185</td>
+      <td>0.077840</td>
     </tr>
     <tr>
       <th>split4_test_score</th>
-      <td>0.146376</td>
-      <td>0.134605</td>
-      <td>0.134260</td>
-      <td>0.144846</td>
-      <td>0.146525</td>
+      <td>0.140699</td>
+      <td>0.133569</td>
+      <td>0.139557</td>
+      <td>0.152427</td>
+      <td>0.143822</td>
     </tr>
     <tr>
       <th>split5_test_score</th>
-      <td>0.106774</td>
-      <td>0.110818</td>
-      <td>0.108646</td>
-      <td>0.103209</td>
-      <td>0.118163</td>
+      <td>0.113387</td>
+      <td>0.114269</td>
+      <td>0.109349</td>
+      <td>0.106754</td>
+      <td>0.116180</td>
     </tr>
     <tr>
       <th>split6_test_score</th>
-      <td>0.087637</td>
-      <td>0.101564</td>
-      <td>0.106001</td>
-      <td>0.108448</td>
-      <td>0.104643</td>
+      <td>0.080712</td>
+      <td>0.095461</td>
+      <td>0.107454</td>
+      <td>0.107903</td>
+      <td>0.108229</td>
     </tr>
     <tr>
       <th>split7_test_score</th>
-      <td>0.069828</td>
-      <td>0.061365</td>
-      <td>0.063359</td>
-      <td>0.078356</td>
-      <td>0.087845</td>
+      <td>0.070899</td>
+      <td>0.067751</td>
+      <td>0.059840</td>
+      <td>0.069572</td>
+      <td>0.078243</td>
     </tr>
     <tr>
       <th>split8_test_score</th>
-      <td>0.103975</td>
-      <td>0.113042</td>
-      <td>0.124293</td>
-      <td>0.122794</td>
-      <td>0.134883</td>
+      <td>0.103105</td>
+      <td>0.112480</td>
+      <td>0.121120</td>
+      <td>0.124410</td>
+      <td>0.127232</td>
     </tr>
     <tr>
       <th>split9_test_score</th>
-      <td>0.119554</td>
-      <td>0.151334</td>
-      <td>0.162009</td>
-      <td>0.169056</td>
-      <td>0.191246</td>
+      <td>0.107526</td>
+      <td>0.157958</td>
+      <td>0.171831</td>
+      <td>0.164579</td>
+      <td>0.184743</td>
     </tr>
   </tbody>
 </table>
@@ -1226,7 +1226,7 @@ <h2>Hyper-parameters tuning and cross validation<a class="headerlink" href="#hyp
 yield a density over a reduced set of bins, and <code class="docutils literal notranslate"><span class="pre">&quot;value_counts&quot;</span></code> yields an
 exhaustive histogram over all the possible values of ratings
 (here 10 different values, from 0.5 to 5).</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.997 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 21.351 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-08-join-aggregation-py">
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/08_join_aggregation.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/09_interpolation_join.html b/dev/auto_examples/09_interpolation_join.html
index 4aede516..b7f63c32 100644
--- a/dev/auto_examples/09_interpolation_join.html
+++ b/dev/auto_examples/09_interpolation_join.html
@@ -756,9 +756,9 @@ <h2>Joining the tables<a class="headerlink" href="#joining-the-tables" title="Li
       <td>42.6</td>
       <td>0.0</td>
       <td>NaN</td>
-      <td>34.050713</td>
-      <td>79.557726</td>
-      <td>0.061708</td>
+      <td>33.273594</td>
+      <td>93.598452</td>
+      <td>-0.143711</td>
     </tr>
     <tr>
       <th>1</th>
@@ -768,9 +768,9 @@ <h2>Joining the tables<a class="headerlink" href="#joining-the-tables" title="Li
       <td>26.9</td>
       <td>NaN</td>
       <td>NaN</td>
-      <td>26.447479</td>
-      <td>80.855013</td>
-      <td>0.282900</td>
+      <td>27.421386</td>
+      <td>145.126263</td>
+      <td>0.032536</td>
     </tr>
     <tr>
       <th>2</th>
@@ -780,9 +780,9 @@ <h2>Joining the tables<a class="headerlink" href="#joining-the-tables" title="Li
       <td>41.6</td>
       <td>NaN</td>
       <td>NaN</td>
-      <td>31.378841</td>
-      <td>29.758804</td>
-      <td>0.061708</td>
+      <td>31.697874</td>
+      <td>29.228029</td>
+      <td>-0.146347</td>
     </tr>
     <tr>
       <th>3</th>
@@ -792,9 +792,9 @@ <h2>Joining the tables<a class="headerlink" href="#joining-the-tables" title="Li
       <td>46.6</td>
       <td>0.0</td>
       <td>NaN</td>
-      <td>33.341582</td>
-      <td>56.433833</td>
-      <td>0.061708</td>
+      <td>33.469607</td>
+      <td>55.614446</td>
+      <td>-0.146347</td>
     </tr>
     <tr>
       <th>4</th>
@@ -804,9 +804,9 @@ <h2>Joining the tables<a class="headerlink" href="#joining-the-tables" title="Li
       <td>36.1</td>
       <td>0.0</td>
       <td>NaN</td>
-      <td>29.703763</td>
-      <td>34.348435</td>
-      <td>0.061708</td>
+      <td>29.672556</td>
+      <td>28.401216</td>
+      <td>0.129800</td>
     </tr>
   </tbody>
 </table>
@@ -922,7 +922,7 @@ <h2>Joining the flights and weather data<a class="headerlink" href="#joining-the
       <td>MI</td>
       <td>42.212059</td>
       <td>-83.348836</td>
-      <td>1.573918</td>
+      <td>0.100304</td>
     </tr>
     <tr>
       <th>1</th>
@@ -934,7 +934,7 @@ <h2>Joining the flights and weather data<a class="headerlink" href="#joining-the
       <td>MI</td>
       <td>42.212059</td>
       <td>-83.348836</td>
-      <td>16.273818</td>
+      <td>15.822712</td>
     </tr>
     <tr>
       <th>2</th>
@@ -946,7 +946,7 @@ <h2>Joining the flights and weather data<a class="headerlink" href="#joining-the
       <td>MI</td>
       <td>42.212059</td>
       <td>-83.348836</td>
-      <td>5.417276</td>
+      <td>5.827000</td>
     </tr>
     <tr>
       <th>3</th>
@@ -958,7 +958,7 @@ <h2>Joining the flights and weather data<a class="headerlink" href="#joining-the
       <td>MI</td>
       <td>42.212059</td>
       <td>-83.348836</td>
-      <td>6.667582</td>
+      <td>6.568854</td>
     </tr>
     <tr>
       <th>4</th>
@@ -970,7 +970,7 @@ <h2>Joining the flights and weather data<a class="headerlink" href="#joining-the
       <td>MI</td>
       <td>42.212059</td>
       <td>-83.348836</td>
-      <td>6.430813</td>
+      <td>7.027463</td>
     </tr>
   </tbody>
 </table>
@@ -990,11 +990,11 @@ <h2>Sanity checks<a class="headerlink" href="#sanity-checks" title="Link to this
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>state
-AK   -4.848166
-WA   -0.189643
-MT    0.126340
-ND    1.035899
-MN    1.665222
+AK   -4.202223
+MT    0.497644
+WA    0.735437
+MN    1.459978
+ND    1.460721
 Name: TMAX, dtype: float64
 </pre></div>
 </div>
@@ -1003,11 +1003,11 @@ <h2>Sanity checks<a class="headerlink" href="#sanity-checks" title="Link to this
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>state
-LA    21.833117
-FL    24.515461
-HI    26.971245
-VI    30.231336
-PR    30.808106
+LA    21.635930
+FL    24.662567
+HI    27.179546
+VI    29.895677
+PR    30.640109
 Name: TMAX, dtype: float64
 </pre></div>
 </div>
@@ -1036,7 +1036,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 <p>We have seen how to fit an <a class="reference internal" href="../generated/skrub.InterpolationJoiner.html#skrub.InterpolationJoiner" title="skrub.InterpolationJoiner"><code class="xref py py-class docutils literal notranslate"><span class="pre">InterpolationJoiner</span></code></a> transformer: we give it a table (the weather data) and a set of matching columns (here date, latitude, longitude) and it learns to predict the other columns’ values  (such as the max daily temperature).
 Then, it transforms tables by <em>predicting</em> values that a matching row would contain, rather than by searching for an actual match.
 It is a generalization of the <a class="reference internal" href="../generated/skrub.fuzzy_join.html#skrub.fuzzy_join" title="skrub.fuzzy_join"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuzzy_join()</span></code></a>, as <a class="reference internal" href="../generated/skrub.fuzzy_join.html#skrub.fuzzy_join" title="skrub.fuzzy_join"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuzzy_join()</span></code></a> is the same thing as an <a class="reference internal" href="../generated/skrub.InterpolationJoiner.html#skrub.InterpolationJoiner" title="skrub.InterpolationJoiner"><code class="xref py py-class docutils literal notranslate"><span class="pre">InterpolationJoiner</span></code></a> where the estimators are 1-nearest-neighbor estimators.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.503 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.568 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-09-interpolation-join-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.1.0?urlpath=lab/tree/notebooks/auto_examples/09_interpolation_join.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
diff --git a/dev/auto_examples/sg_execution_times.html b/dev/auto_examples/sg_execution_times.html
index e8f67f22..917199da 100644
--- a/dev/auto_examples/sg_execution_times.html
+++ b/dev/auto_examples/sg_execution_times.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://skrub-data.github.io/stable/auto_examples/sg_execution_times.html" />
 <meta property="og:site_name" content="skrub" />
-<meta property="og:description" content="24:52.633 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns( 07_multiple_key_join.py), 11:29.384, 0.0,..." />
+<meta property="og:description" content="23:31.339 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns( 07_multiple_key_join.py), 10:56.053, 0.0,..." />
 <meta property="og:image" content="https://skrub-data.github.io/stable/_static/skrub.svg" />
 <meta property="og:image:alt" content="skrub" />
-<meta name="description" content="24:52.633 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns( 07_multiple_key_join.py), 11:29.384, 0.0,..." />
+<meta name="description" content="23:31.339 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns( 07_multiple_key_join.py), 10:56.053, 0.0,..." />
 
     <title>Computation times &#8212; skrub</title>
   
@@ -471,7 +471,7 @@
                   
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>24:52.633</strong> total execution time for 9 files <strong>from auto_examples</strong>:</p>
+<p><strong>23:31.339</strong> total execution time for 9 files <strong>from auto_examples</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -493,39 +493,39 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="07_multiple_key_join.html#sphx-glr-auto-examples-07-multiple-key-join-py"><span class="std std-ref">Spatial join for flight data: Joining across multiple columns</span></a> (<code class="docutils literal notranslate"><span class="pre">07_multiple_key_join.py</span></code>)</p></td>
-<td><p>11:29.384</p></td>
+<td><p>10:56.053</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="06_ken_embeddings.html#sphx-glr-auto-examples-06-ken-embeddings-py"><span class="std std-ref">Wikipedia embeddings to enrich the data</span></a> (<code class="docutils literal notranslate"><span class="pre">06_ken_embeddings.py</span></code>)</p></td>
-<td><p>11:07.838</p></td>
+<td><p>10:30.856</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="01_encodings.html#sphx-glr-auto-examples-01-encodings-py"><span class="std std-ref">Encoding: from a dataframe to a numerical matrix for machine learning</span></a> (<code class="docutils literal notranslate"><span class="pre">01_encodings.py</span></code>)</p></td>
-<td><p>01:19.568</p></td>
+<td><p>01:08.670</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="04_fuzzy_joining.html#sphx-glr-auto-examples-04-fuzzy-joining-py"><span class="std std-ref">Fuzzy joining dirty tables with the Joiner</span></a> (<code class="docutils literal notranslate"><span class="pre">04_fuzzy_joining.py</span></code>)</p></td>
-<td><p>00:19.674</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="08_join_aggregation.html#sphx-glr-auto-examples-08-join-aggregation-py"><span class="std std-ref">Self-aggregation on MovieLens</span></a> (<code class="docutils literal notranslate"><span class="pre">08_join_aggregation.py</span></code>)</p></td>
+<td><p>00:21.351</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="08_join_aggregation.html#sphx-glr-auto-examples-08-join-aggregation-py"><span class="std std-ref">Self-aggregation on MovieLens</span></a> (<code class="docutils literal notranslate"><span class="pre">08_join_aggregation.py</span></code>)</p></td>
-<td><p>00:18.997</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="04_fuzzy_joining.html#sphx-glr-auto-examples-04-fuzzy-joining-py"><span class="std std-ref">Fuzzy joining dirty tables with the Joiner</span></a> (<code class="docutils literal notranslate"><span class="pre">04_fuzzy_joining.py</span></code>)</p></td>
+<td><p>00:17.135</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="09_interpolation_join.html#sphx-glr-auto-examples-09-interpolation-join-py"><span class="std std-ref">Interpolation join: infer missing rows when joining two tables</span></a> (<code class="docutils literal notranslate"><span class="pre">09_interpolation_join.py</span></code>)</p></td>
-<td><p>00:05.503</p></td>
+<td><p>00:06.568</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="03_datetime_encoder.html#sphx-glr-auto-examples-03-datetime-encoder-py"><span class="std std-ref">Handling datetime features with the DatetimeEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">03_datetime_encoder.py</span></code>)</p></td>
-<td><p>00:04.948</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="05_deduplication.html#sphx-glr-auto-examples-05-deduplication-py"><span class="std std-ref">Deduplicating misspelled categories</span></a> (<code class="docutils literal notranslate"><span class="pre">05_deduplication.py</span></code>)</p></td>
+<td><p>00:04.909</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="05_deduplication.html#sphx-glr-auto-examples-05-deduplication-py"><span class="std std-ref">Deduplicating misspelled categories</span></a> (<code class="docutils literal notranslate"><span class="pre">05_deduplication.py</span></code>)</p></td>
-<td><p>00:04.680</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="03_datetime_encoder.html#sphx-glr-auto-examples-03-datetime-encoder-py"><span class="std std-ref">Handling datetime features with the DatetimeEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">03_datetime_encoder.py</span></code>)</p></td>
+<td><p>00:04.149</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="02_feature_interpretation_with_gapencoder.html#sphx-glr-auto-examples-02-feature-interpretation-with-gapencoder-py"><span class="std std-ref">Feature interpretation with the GapEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">02_feature_interpretation_with_gapencoder.py</span></code>)</p></td>
-<td><p>00:02.041</p></td>
+<td><p>00:01.648</p></td>
 <td><p>0.0</p></td>
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diff --git a/dev/generated/skrub.AggJoiner.html b/dev/generated/skrub.AggJoiner.html
index 11ccc429..e0430bdb 100644
--- a/dev/generated/skrub.AggJoiner.html
+++ b/dev/generated/skrub.AggJoiner.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.AggJoiner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">AggJoiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">AggJoiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner" title="Link to this definition">#</a></dt>
 <dd><p>Aggregate auxiliary dataframes before joining them on a base dataframe.</p>
 <p>Apply numerical and categorical aggregation operations on the columns
 to aggregate, selected by dtypes. See the list of supported operations
@@ -652,7 +652,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.check_input">
-<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.check_input" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.check_input" title="Link to this definition">#</a></dt>
 <dd><p>Perform a check on column names data type and suffixes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -666,7 +666,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.fit" title="Link to this definition">#</a></dt>
 <dd><p>Aggregate auxiliary tables based on the main keys.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -690,7 +690,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -716,7 +716,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -733,7 +733,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -754,7 +754,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -781,7 +781,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -805,7 +805,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggJoiner.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggJoiner.transform" title="Link to this definition">#</a></dt>
 <dd><p>Left-join pre-aggregated tables on <cite>X</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.AggTarget.html b/dev/generated/skrub.AggTarget.html
index f6424cf7..860d4623 100644
--- a/dev/generated/skrub.AggTarget.html
+++ b/dev/generated/skrub.AggTarget.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.AggTarget">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">AggTarget</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">AggTarget</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget" title="Link to this definition">#</a></dt>
 <dd><p>Aggregate a target <code class="docutils literal notranslate"><span class="pre">y</span></code> before joining its aggregation on a base dataframe.</p>
 <p>Accepts <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> or <code class="xref py py-class docutils literal notranslate"><span class="pre">polars.DataFrame</span></code> inputs.</p>
 <dl class="field-list">
@@ -638,7 +638,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.check_input">
-<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.check_input" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.check_input" title="Link to this definition">#</a></dt>
 <dd><p>Perform a check on column names data type and suffixes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -660,7 +660,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.fit" title="Link to this definition">#</a></dt>
 <dd><p>Aggregate the target <code class="docutils literal notranslate"><span class="pre">y</span></code> based on keys from <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -689,7 +689,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -715,7 +715,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -732,7 +732,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -753,7 +753,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -780,7 +780,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -804,7 +804,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.AggTarget.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_agg_joiner.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_agg_joiner.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.AggTarget.transform" title="Link to this definition">#</a></dt>
 <dd><p>Left-join pre-aggregated tables on <cite>X</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.DatetimeEncoder.html b/dev/generated/skrub.DatetimeEncoder.html
index 5fb3716b..8792e8d6 100644
--- a/dev/generated/skrub.DatetimeEncoder.html
+++ b/dev/generated/skrub.DatetimeEncoder.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">DatetimeEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hour'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_day_of_the_week</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_total_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'coerce'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_datetime_encoder.py#L498"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">DatetimeEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hour'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_day_of_the_week</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_total_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'coerce'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_datetime_encoder.py#L498"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Transforms each datetime column into several numeric columns     for temporal features (e.g year, month, day…).</p>
 <p>If the dates are timezone aware, all the features extracted will correspond
 to the provided timezone.</p>
@@ -641,7 +641,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_datetime_encoder.py#L584"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_datetime_encoder.py#L584"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the instance to X.</p>
 <p>Select datetime-parsable columns and generate the list of
 datetime feature to extract.</p>
@@ -667,7 +667,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -693,7 +693,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_datetime_encoder.py#L734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_datetime_encoder.py#L734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>Feature names are formatted like: “&lt;column_name&gt;_&lt;new_feature&gt;”
 if the original data has column names, otherwise with format
@@ -718,7 +718,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -735,7 +735,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -756,7 +756,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -783,7 +783,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -807,7 +807,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DatetimeEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_datetime_encoder.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_datetime_encoder.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DatetimeEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform <code class="docutils literal notranslate"><span class="pre">X</span></code> by replacing each datetime column with         corresponding numerical features.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.DropCols.html b/dev/generated/skrub.DropCols.html
index 0306e216..86991765 100644
--- a/dev/generated/skrub.DropCols.html
+++ b/dev/generated/skrub.DropCols.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.DropCols">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">DropCols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cols</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">DropCols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cols</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols" title="Link to this definition">#</a></dt>
 <dd><p>Drop a subset of a DataFrame’s columns.</p>
 <p>The other columns are kept in their original order. A <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> is
 raised if any of the provided column names are not in the dataframe.</p>
@@ -602,7 +602,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the transformer.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -625,7 +625,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -651,7 +651,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -668,7 +668,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -689,7 +689,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -716,7 +716,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -740,7 +740,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.DropCols.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.DropCols.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a dataframe by dropping columns.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.GapEncoder.html b/dev/generated/skrub.GapEncoder.html
index 858a14ec..d6b17613 100644
--- a/dev/generated/skrub.GapEncoder.html
+++ b/dev/generated/skrub.GapEncoder.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.GapEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">GapEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_shape_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_scale_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rescale_rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing_n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4096</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'char'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_words</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rescale_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter_e_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero_impute'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">GapEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_shape_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_scale_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rescale_rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing_n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4096</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'char'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_words</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rescale_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter_e_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero_impute'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Constructs latent topics with continuous encoding.</p>
 <p>This encoder can be understood as a continuous encoding on a set of latent
 categories estimated from the data. The latent categories are built by
@@ -743,7 +743,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the instance on X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -765,7 +765,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -791,7 +791,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L951"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L951"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Return the labels that best summarize the learned components/topics.</p>
 <p>For each topic, labels with the highest activations are selected.</p>
 <dl class="field-list">
@@ -827,7 +827,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -844,7 +844,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -865,7 +865,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Partial fit this instance on X.</p>
 <p>To be used in an online learning procedure where batches of data are
 coming one by one.</p>
@@ -889,7 +889,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.score" title="Link to this definition">#</a></dt>
 <dd><p>Score this instance on <cite>X</cite>.</p>
 <p>Returns the sum over the columns of <cite>X</cite> of the Kullback-Leibler
 divergence between the n-grams counts matrix <cite>V</cite> of <cite>X</cite>, and its
@@ -914,7 +914,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -941,7 +941,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -965,7 +965,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.GapEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_gap_encoder.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_gap_encoder.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.GapEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Return the encoded vectors (activations) <cite>H</cite> of input strings in <cite>X</cite>.</p>
 <p>Given the learnt topics <cite>W</cite>, the activations <cite>H</cite> are tuned to fit
 <code class="docutils literal notranslate"><span class="pre">V</span> <span class="pre">=</span> <span class="pre">HW</span></code>. When <cite>X</cite> has several columns, they are encoded separately
diff --git a/dev/generated/skrub.InterpolationJoiner.html b/dev/generated/skrub.InterpolationJoiner.html
index ad4c1726..7f65adcd 100644
--- a/dev/generated/skrub.InterpolationJoiner.html
+++ b/dev/generated/skrub.InterpolationJoiner.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">InterpolationJoiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">regressor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">HistGradientBoostingRegressor()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">HistGradientBoostingClassifier()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">TableVectorizer(high_cardinality_transformer=MinHashEncoder())</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_estimator_failure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_interpolation_joiner.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">InterpolationJoiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">regressor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">HistGradientBoostingRegressor()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">HistGradientBoostingClassifier()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">TableVectorizer(high_cardinality_transformer=MinHashEncoder())</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_estimator_failure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_interpolation_joiner.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner" title="Link to this definition">#</a></dt>
 <dd><p>Join with a table augmented by machine-learning predictions.</p>
 <p>This is similar to a usual equi-join, but instead of looking for actual
 rows in the right table that satisfy the join condition, we estimate what
@@ -722,7 +722,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_interpolation_joiner.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_interpolation_joiner.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimators to the <cite>aux_table</cite> provided during initialization.</p>
 <p><cite>X</cite> and <cite>y</cite> are mostly for scikit-learn compatibility.</p>
 <dl class="field-list">
@@ -747,7 +747,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -773,7 +773,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -790,7 +790,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -811,7 +811,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -838,7 +838,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -862,7 +862,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.InterpolationJoiner.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_interpolation_joiner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_interpolation_joiner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.InterpolationJoiner.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a table by joining inferred values to it.</p>
 <p>The values of the <cite>main_key</cite> columns in <cite>X</cite> (the main table) are used
 to predict likely values for the contents of a matching row in
diff --git a/dev/generated/skrub.Joiner.html b/dev/generated/skrub.Joiner.html
index 99c2314d..2c397300 100644
--- a/dev/generated/skrub.Joiner.html
+++ b/dev/generated/skrub.Joiner.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.Joiner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">Joiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_dist=inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_dist='random_pairs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string_encoder=Pipeline(steps=[('functiontransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">FunctionTransformer(func=&lt;function</span> <span class="pre">_as_str&gt;))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('hashingvectorizer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">HashingVectorizer(analyzer='char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('tfidftransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TfidfTransformer())])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_match_info=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_joiner.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">Joiner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_table</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_dist=inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_dist='random_pairs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string_encoder=Pipeline(steps=[('functiontransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">FunctionTransformer(func=&lt;function</span> <span class="pre">_as_str&gt;))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('hashingvectorizer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">HashingVectorizer(analyzer='char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('tfidftransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TfidfTransformer())])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_match_info=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_joiner.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner" title="Link to this definition">#</a></dt>
 <dd><p>Augment features in a main table by fuzzy-joining an auxiliary table to it.</p>
 <p>This transformer is initialized with an auxiliary table <code class="docutils literal notranslate"><span class="pre">aux_table</span></code>. It
 transforms a main table by joining it, with approximate (“fuzzy”) matching,
@@ -710,7 +710,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_joiner.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_joiner.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the instance to the main table.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -732,7 +732,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -758,7 +758,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -775,7 +775,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -796,7 +796,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -823,7 +823,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -847,7 +847,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.Joiner.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_joiner.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_joiner.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.Joiner.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform <cite>X</cite> using the specified encoding scheme.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.MinHashEncoder.html b/dev/generated/skrub.MinHashEncoder.html
index 1b1ccf0c..ddb7ddbf 100644
--- a/dev/generated/skrub.MinHashEncoder.html
+++ b/dev/generated/skrub.MinHashEncoder.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">MinHashEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minmax_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero_impute'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_minhash_encoder.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">MinHashEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minmax_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero_impute'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_minhash_encoder.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Encode string categorical features by applying the MinHash method to n-gram     decompositions of strings.</p>
 <p>The principle is as follows:</p>
 <ol class="arabic simple">
@@ -669,7 +669,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_minhash_encoder.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_minhash_encoder.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the MinHashEncoder to <cite>X</cite>.</p>
 <p>In practice, just initializes a dictionary
 to store encodings to speed up computation.</p>
@@ -693,7 +693,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -719,7 +719,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_minhash_encoder.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_minhash_encoder.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The output feature names look like:
 <code class="docutils literal notranslate"><span class="pre">[&quot;x0_0&quot;,</span> <span class="pre">&quot;x0_1&quot;,</span> <span class="pre">...,</span> <span class="pre">&quot;x0_(n_components</span> <span class="pre">-</span> <span class="pre">1)&quot;,</span>
@@ -751,7 +751,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -768,7 +768,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -789,7 +789,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -816,7 +816,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -840,7 +840,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.MinHashEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_minhash_encoder.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_minhash_encoder.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.MinHashEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform <cite>X</cite> using specified encoding scheme.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.SelectCols.html b/dev/generated/skrub.SelectCols.html
index 4d5e318e..dbc5bc0d 100644
--- a/dev/generated/skrub.SelectCols.html
+++ b/dev/generated/skrub.SelectCols.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.SelectCols">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">SelectCols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cols</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">SelectCols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cols</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols" title="Link to this definition">#</a></dt>
 <dd><p>Select a subset of a DataFrame’s columns.</p>
 <p>A <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> is raised if any of the provided column names are not in
 the dataframe.</p>
@@ -602,7 +602,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the transformer.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -625,7 +625,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -651,7 +651,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -668,7 +668,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -689,7 +689,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -716,7 +716,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -740,7 +740,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SelectCols.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_select_cols.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_select_cols.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SelectCols.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a dataframe by selecting columns.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.SimilarityEncoder.html b/dev/generated/skrub.SimilarityEncoder.html
index 42a3eac5..2d2cbf4d 100644
--- a/dev/generated/skrub.SimilarityEncoder.html
+++ b/dev/generated/skrub.SimilarityEncoder.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">SimilarityEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='char'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='ignore'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing_dim=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_similarity_encoder.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">SimilarityEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='char'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='ignore'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_missing=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hashing_dim=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_similarity_encoder.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Encode string categories to a similarity matrix, to capture fuzziness across a few categories.</p>
 <p>The input to this transformer should be an array-like of strings.
 The method is based on calculating the morphological similarities
@@ -708,7 +708,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_similarity_encoder.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_similarity_encoder.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the instance to <cite>X</cite>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -730,7 +730,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <cite>X</cite> and <cite>y</cite> with optional parameters <cite>fit_params</cite>
 and returns a transformed version of <cite>X</cite>.</p>
@@ -756,7 +756,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/preprocessing/_encoders.py#L1190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/preprocessing/_encoders.py#L1190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -784,7 +784,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -801,7 +801,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -828,7 +828,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/preprocessing/_encoders.py#L1071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/preprocessing/_encoders.py#L1071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Convert the data back to the original representation.</p>
 <p>When unknown categories are encountered (all zeros in the
 one-hot encoding), <code class="docutils literal notranslate"><span class="pre">None</span></code> is used to represent this category. If the
@@ -854,7 +854,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -881,7 +881,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -905,7 +905,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.set_config.html#sklearn.set_config" title="(in scikit-learn v1.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code></a>).
@@ -948,7 +948,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.SimilarityEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_similarity_encoder.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_similarity_encoder.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.SimilarityEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform <cite>X</cite> using specified encoding scheme.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.TableVectorizer.html b/dev/generated/skrub.TableVectorizer.html
index 30aaa06f..43b98e1c 100644
--- a/dev/generated/skrub.TableVectorizer.html
+++ b/dev/generated/skrub.TableVectorizer.html
@@ -543,7 +543,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.TableVectorizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">TableVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cardinality_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low_cardinality_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OneHotEncoder(drop='if_binary',</span> <span class="pre">handle_unknown='ignore',</span> <span class="pre">sparse_output=False)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high_cardinality_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">GapEncoder(n_components=30)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numerical_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'passthrough'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datetime_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DatetimeEncoder()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specific_transformers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_cast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'passthrough'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_table_vectorizer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">TableVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cardinality_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low_cardinality_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OneHotEncoder(drop='if_binary',</span> <span class="pre">handle_unknown='ignore',</span> <span class="pre">sparse_output=False)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high_cardinality_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">GapEncoder(n_components=30)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numerical_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'passthrough'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datetime_transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DatetimeEncoder()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specific_transformers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_cast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'passthrough'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_table_vectorizer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer" title="Link to this definition">#</a></dt>
 <dd><p>Automatically transform a heterogeneous dataframe to a numerical array.</p>
 <p>Easily transforms a heterogeneous data table
 (such as a <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a>) to a numerical array for machine
@@ -758,7 +758,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 </table>
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_table_vectorizer.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_table_vectorizer.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers using X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -781,7 +781,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_table_vectorizer.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_table_vectorizer.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers, transform the data, and concatenate the results.</p>
 <p>In practice, it:
 - Converts features to their best possible types for scikit-learn estimators</p>
@@ -817,7 +817,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_table_vectorizer.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_table_vectorizer.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Return clean feature names.</p>
 <p>Feature names are formatted like:
 “&lt;column_name&gt;_&lt;value&gt;” if encoded by OneHotEncoder or alike,
@@ -840,7 +840,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_metadata_requests.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference external" href="https://scikit-learn.org/stable/metadata_routing.html#metadata-routing" title="(in scikit-learn v1.3)"><span class="xref std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -857,7 +857,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -897,7 +897,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/utils/_set_output.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py" title="(in scikit-learn v1.3)"><span>Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -924,7 +924,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/../sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -957,7 +957,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="skrub.TableVectorizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_table_vectorizer.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_table_vectorizer.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.TableVectorizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform <code class="docutils literal notranslate"><span class="pre">X</span></code> by applying the fitted transformers on the columns.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.datasets.DatasetAll.html b/dev/generated/skrub.datasets.DatasetAll.html
index 86b36fc5..87efa49b 100644
--- a/dev/generated/skrub.datasets.DatasetAll.html
+++ b/dev/generated/skrub.datasets.DatasetAll.html
@@ -474,7 +474,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.datasets.DatasetAll">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">DatasetAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.DatasetAll" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">DatasetAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.DatasetAll" title="Link to this definition">#</a></dt>
 <dd><p>Represents a dataset and its information.
 With this state, the dataset is loaded in memory as a DataFrame (<cite>X</cite> and <cite>y</cite>).
 Additional information such as <cite>path</cite> and <cite>read_csv_kwargs</cite> are provided
diff --git a/dev/generated/skrub.datasets.DatasetInfoOnly.html b/dev/generated/skrub.datasets.DatasetInfoOnly.html
index 4ef12239..11d0ca4f 100644
--- a/dev/generated/skrub.datasets.DatasetInfoOnly.html
+++ b/dev/generated/skrub.datasets.DatasetInfoOnly.html
@@ -474,7 +474,7 @@ <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">
 <p class="side-comment">Usage examples at the bottom of this page.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="skrub.datasets.DatasetInfoOnly">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">DatasetInfoOnly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.DatasetInfoOnly" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">DatasetInfoOnly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.DatasetInfoOnly" title="Link to this definition">#</a></dt>
 <dd><p>Represents a dataset and its information.
 With this state, the dataset is NOT loaded in memory, but can be read
 with <cite>path</cite> and <cite>read_csv_kwargs</cite>, as such:</p>
diff --git a/dev/generated/skrub.datasets.fetch_drug_directory.html b/dev/generated/skrub.datasets.fetch_drug_directory.html
index cb412c24..edd368ac 100644
--- a/dev/generated/skrub.datasets.fetch_drug_directory.html
+++ b/dev/generated/skrub.datasets.fetch_drug_directory.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_drug_directory<a class="headerlink" href="#skrub-datasets-fetch-drug-directory" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_drug_directory">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_drug_directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_drug_directory" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_drug_directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_drug_directory" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the drug directory dataset (classification), available at <a class="reference external" href="https://openml.org/d/43044">https://openml.org/d/43044</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>Product listing data submitted to the U.S. FDA for all unfinished,
diff --git a/dev/generated/skrub.datasets.fetch_employee_salaries.html b/dev/generated/skrub.datasets.fetch_employee_salaries.html
index 6bc69615..61e7521d 100644
--- a/dev/generated/skrub.datasets.fetch_employee_salaries.html
+++ b/dev/generated/skrub.datasets.fetch_employee_salaries.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_employee_salaries<a class="headerlink" href="#skrub-datasets-fetch-employee-salaries" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_employee_salaries">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_employee_salaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_linked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_irrelevant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload_job_titles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L726"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_employee_salaries" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_employee_salaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_linked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_irrelevant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload_job_titles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L726"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_employee_salaries" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the employee salaries dataset (regression), available at <a class="reference external" href="https://openml.org/d/42125">https://openml.org/d/42125</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>Annual salary information including gross pay and overtime pay for all
diff --git a/dev/generated/skrub.datasets.fetch_ken_embeddings.html b/dev/generated/skrub.datasets.fetch_ken_embeddings.html
index dbf1ec04..3ea3161b 100644
--- a/dev/generated/skrub.datasets.fetch_ken_embeddings.html
+++ b/dev/generated/skrub.datasets.fetch_ken_embeddings.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_ken_embeddings<a class="headerlink" href="#skrub-datasets-fetch-ken-embeddings" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_ken_embeddings">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_embeddings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">search_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_table_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all_entities'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_type_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pca_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_ken_embeddings.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_embeddings" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_embeddings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">search_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_table_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all_entities'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_type_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pca_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_ken_embeddings.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_embeddings" title="Link to this definition">#</a></dt>
 <dd><p>Download Wikipedia embeddings by type.</p>
 <p>More details on the embeddings can be found on
 <a class="reference external" href="https://soda-inria.github.io/ken_embeddings/">https://soda-inria.github.io/ken_embeddings/</a>.</p>
diff --git a/dev/generated/skrub.datasets.fetch_ken_table_aliases.html b/dev/generated/skrub.datasets.fetch_ken_table_aliases.html
index 44060fa2..1cbda383 100644
--- a/dev/generated/skrub.datasets.fetch_ken_table_aliases.html
+++ b/dev/generated/skrub.datasets.fetch_ken_table_aliases.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_ken_table_aliases<a class="headerlink" href="#skrub-datasets-fetch-ken-table-aliases" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_ken_table_aliases">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_table_aliases</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_ken_embeddings.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_table_aliases" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_table_aliases</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_ken_embeddings.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_table_aliases" title="Link to this definition">#</a></dt>
 <dd><p>Get the supported aliases of embedded KEN entities tables.</p>
 <p>These aliases can be using in subsequent functions (see section <em>See Also</em>).</p>
 <dl class="field-list simple">
diff --git a/dev/generated/skrub.datasets.fetch_ken_types.html b/dev/generated/skrub.datasets.fetch_ken_types.html
index e3ff1081..bd64d4fe 100644
--- a/dev/generated/skrub.datasets.fetch_ken_types.html
+++ b/dev/generated/skrub.datasets.fetch_ken_types.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_ken_types<a class="headerlink" href="#skrub-datasets-fetch-ken-types" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_ken_types">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_types</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_table_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all_entities'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_ken_embeddings.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_types" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_ken_types</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_table_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all_entities'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_ken_embeddings.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_ken_types" title="Link to this definition">#</a></dt>
 <dd><p>Helper function to search for KEN entity types.</p>
 <p>The result can then be used with fetch_ken_embeddings.</p>
 <dl class="field-list">
diff --git a/dev/generated/skrub.datasets.fetch_medical_charge.html b/dev/generated/skrub.datasets.fetch_medical_charge.html
index 8165acb7..5db5a73a 100644
--- a/dev/generated/skrub.datasets.fetch_medical_charge.html
+++ b/dev/generated/skrub.datasets.fetch_medical_charge.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_medical_charge<a class="headerlink" href="#skrub-datasets-fetch-medical-charge" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_medical_charge">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_medical_charge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_medical_charge" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_medical_charge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_medical_charge" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the medical charge dataset (regression), available at <a class="reference external" href="https://openml.org/d/42720">https://openml.org/d/42720</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>The Inpatient Utilization and Payment Public Use File (Inpatient PUF)
diff --git a/dev/generated/skrub.datasets.fetch_midwest_survey.html b/dev/generated/skrub.datasets.fetch_midwest_survey.html
index f9f10cad..d741479b 100644
--- a/dev/generated/skrub.datasets.fetch_midwest_survey.html
+++ b/dev/generated/skrub.datasets.fetch_midwest_survey.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_midwest_survey<a class="headerlink" href="#skrub-datasets-fetch-midwest-survey" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_midwest_survey">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_midwest_survey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_midwest_survey" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_midwest_survey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_midwest_survey" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the midwest survey dataset (classification), available at <a class="reference external" href="https://openml.org/d/42805">https://openml.org/d/42805</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>Survey to know if people self-identify as Midwesterners.</p>
diff --git a/dev/generated/skrub.datasets.fetch_movielens.html b/dev/generated/skrub.datasets.fetch_movielens.html
index ad6dcd0e..78b84318 100644
--- a/dev/generated/skrub.datasets.fetch_movielens.html
+++ b/dev/generated/skrub.datasets.fetch_movielens.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_movielens<a class="headerlink" href="#skrub-datasets-fetch-movielens" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_movielens">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_movielens</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ratings'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_movielens" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_movielens</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ratings'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_movielens" title="Link to this definition">#</a></dt>
 <dd><p>Fetches a dataset from Movielens.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/generated/skrub.datasets.fetch_open_payments.html b/dev/generated/skrub.datasets.fetch_open_payments.html
index fc664bfc..d48c0472 100644
--- a/dev/generated/skrub.datasets.fetch_open_payments.html
+++ b/dev/generated/skrub.datasets.fetch_open_payments.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_open_payments<a class="headerlink" href="#skrub-datasets-fetch-open-payments" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_open_payments">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_open_payments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_open_payments" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_open_payments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_open_payments" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the open payments dataset (classification), available at <a class="reference external" href="https://openml.org/d/42738">https://openml.org/d/42738</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>Payments given by healthcare manufacturing companies to medical doctors
diff --git a/dev/generated/skrub.datasets.fetch_road_safety.html b/dev/generated/skrub.datasets.fetch_road_safety.html
index e220981a..8ded3d51 100644
--- a/dev/generated/skrub.datasets.fetch_road_safety.html
+++ b/dev/generated/skrub.datasets.fetch_road_safety.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_road_safety<a class="headerlink" href="#skrub-datasets-fetch-road-safety" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_road_safety">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_road_safety</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_road_safety" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_road_safety</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_road_safety" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the road safety dataset (classification), available at <a class="reference external" href="https://openml.org/d/42803">https://openml.org/d/42803</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>Data reported to the police about the circumstances of personal injury
diff --git a/dev/generated/skrub.datasets.fetch_traffic_violations.html b/dev/generated/skrub.datasets.fetch_traffic_violations.html
index 01e80003..3dd90f19 100644
--- a/dev/generated/skrub.datasets.fetch_traffic_violations.html
+++ b/dev/generated/skrub.datasets.fetch_traffic_violations.html
@@ -540,7 +540,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_traffic_violations<a class="headerlink" href="#skrub-datasets-fetch-traffic-violations" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_traffic_violations">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_traffic_violations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_traffic_violations" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_traffic_violations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_traffic_violations" title="Link to this definition">#</a></dt>
 <dd><p>Fetches the traffic violations dataset (classification), available at <a class="reference external" href="https://openml.org/d/42132">https://openml.org/d/42132</a></p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>This dataset contains traffic violation information from all electronic
diff --git a/dev/generated/skrub.datasets.fetch_world_bank_indicator.html b/dev/generated/skrub.datasets.fetch_world_bank_indicator.html
index af08c439..97c5dd1c 100644
--- a/dev/generated/skrub.datasets.fetch_world_bank_indicator.html
+++ b/dev/generated/skrub.datasets.fetch_world_bank_indicator.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.fetch_world_bank_indicator<a class="headerlink" href="#skrub-datasets-fetch-world-bank-indicator" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.fetch_world_bank_indicator">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_world_bank_indicator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indicator_id</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_fetching.py#L1006"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_world_bank_indicator" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_world_bank_indicator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indicator_id</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_dataframe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_fetching.py#L1006"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.fetch_world_bank_indicator" title="Link to this definition">#</a></dt>
 <dd><p>Fetches a dataset of an indicator from the World Bank open data platform.</p>
 <dl class="simple">
 <dt>Description of the dataset:</dt><dd><p>The dataset contains two columns: the indicator value and the
diff --git a/dev/generated/skrub.datasets.make_deduplication_data.html b/dev/generated/skrub.datasets.make_deduplication_data.html
index d4d59358..61ae8225 100644
--- a/dev/generated/skrub.datasets.make_deduplication_data.html
+++ b/dev/generated/skrub.datasets.make_deduplication_data.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub.datasets</span></code>.make_deduplication_data<a class="headerlink" href="#skrub-datasets-make-deduplication-data" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.datasets.make_deduplication_data">
-<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">make_deduplication_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entries_per_example</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_mistake_per_letter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/datasets/_generating.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.make_deduplication_data" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.datasets.</span></span><span class="sig-name descname"><span class="pre">make_deduplication_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entries_per_example</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_mistake_per_letter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/datasets/_generating.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.datasets.make_deduplication_data" title="Link to this definition">#</a></dt>
 <dd><p>Duplicates examples with spelling mistakes.</p>
 <p>Characters are misspelled with probability <cite>prob_mistake_per_letter</cite>.</p>
 <dl class="field-list">
diff --git a/dev/generated/skrub.deduplicate.html b/dev/generated/skrub.deduplicate.html
index ff04ce52..b3ac0428 100644
--- a/dev/generated/skrub.deduplicate.html
+++ b/dev/generated/skrub.deduplicate.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub</span></code>.deduplicate<a class="headerlink" href="#skrub-deduplicate" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.deduplicate">
-<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">deduplicate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_deduplicate.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.deduplicate" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">deduplicate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_deduplicate.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.deduplicate" title="Link to this definition">#</a></dt>
 <dd><p>Deduplicate categorical data by hierarchically clustering similar strings.</p>
 <p>This works best if there is a number of underlying categories that
 sometimes appear in the data with small variations and/or misspellings.</p>
diff --git a/dev/generated/skrub.fuzzy_join.html b/dev/generated/skrub.fuzzy_join.html
index 74306d43..044e9294 100644
--- a/dev/generated/skrub.fuzzy_join.html
+++ b/dev/generated/skrub.fuzzy_join.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub</span></code>.fuzzy_join<a class="headerlink" href="#skrub-fuzzy-join" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.fuzzy_join">
-<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">fuzzy_join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">left</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">left_on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right_on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_dist=inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_dist='random_pairs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string_encoder=Pipeline(steps=[('functiontransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">FunctionTransformer(func=&lt;function</span> <span class="pre">_as_str&gt;))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('hashingvectorizer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">HashingVectorizer(analyzer='char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('tfidftransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TfidfTransformer())])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_match_info=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_unmatched=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_fuzzy_join.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.fuzzy_join" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">fuzzy_join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">left</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">left_on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right_on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_dist=inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_dist='random_pairs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string_encoder=Pipeline(steps=[('functiontransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">FunctionTransformer(func=&lt;function</span> <span class="pre">_as_str&gt;))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('hashingvectorizer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">HashingVectorizer(analyzer='char_wb'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">4)))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">('tfidftransformer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TfidfTransformer())])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_match_info=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_unmatched=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_fuzzy_join.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.fuzzy_join" title="Link to this definition">#</a></dt>
 <dd><p>Fuzzy (approximate) join.</p>
 <p>Rows in the left table are joined to their closest match from the right
 table. The resulting table has the same rows (in the same order) as the
diff --git a/dev/generated/skrub.to_datetime.html b/dev/generated/skrub.to_datetime.html
index ca4261c6..d2fad3ed 100644
--- a/dev/generated/skrub.to_datetime.html
+++ b/dev/generated/skrub.to_datetime.html
@@ -542,7 +542,7 @@
 <h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">skrub</span></code>.to_datetime<a class="headerlink" href="#skrub-to-datetime" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skrub.to_datetime">
-<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">to_datetime</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'coerce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/e8950b9/skrub/_datetime_encoder.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.to_datetime" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skrub.</span></span><span class="sig-name descname"><span class="pre">to_datetime</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'coerce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/skrub-data/skrub/blob/80811ed/skrub/_datetime_encoder.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skrub.to_datetime" title="Link to this definition">#</a></dt>
 <dd><p>Convert the columns of a dataframe or 2d array into a datetime representation.</p>
 <p>This function augments <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html#pandas.to_datetime" title="(in pandas v2.1.4)"><code class="xref py py-func docutils literal notranslate"><span class="pre">pandas.to_datetime()</span></code></a> by supporting dataframes
 and 2d array inputs. It only attempts to convert columns whose dtype are
diff --git a/dev/install.html b/dev/install.html
index 140cff19..2e14a719 100644
--- a/dev/install.html
+++ b/dev/install.html
@@ -497,12 +497,14 @@ <h1>Installing<a class="headerlink" href="#installing" title="Link to this headi
 </div>
 </div>
       <div class="tab-pane fade" id="conda-tab-pane" role="tabpanel" aria-labelledby="conda-tab" tabindex="0">
-        <hr /><div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>conda<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>skrub
+        <hr />
+<strong>This will not work yet!</strong> The package is still waiting to be published on conda-forge. You can follow progress <a href="https://github.com/conda-forge/staged-recipes/pull/24766">here</a>.<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>conda<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>skrub
 </pre></div>
 </div>
 </div>
 <div class="tab-pane fade" id="mamba-tab-pane" role="tabpanel" aria-labelledby="mamba-tab" tabindex="0">
-    <hr /><div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>mamba<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>skrub
+    <hr />
+<strong>This will not work yet!</strong> The package is still waiting to be published on conda-forge. You can follow progress <a href="https://github.com/conda-forge/staged-recipes/pull/24766">here</a>.<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>mamba<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>skrub
 </pre></div>
 </div>
 </div>
diff --git a/dev/lite/api/contents/all.json b/dev/lite/api/contents/all.json
index 63a91204..7ce88158 100644
--- a/dev/lite/api/contents/all.json
+++ b/dev/lite/api/contents/all.json
@@ -2,11 +2,11 @@
   "content": [
     {
       "content": null,
-      "created": "2023-12-14T17:33:29.263328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:29.263328Z",
+      "last_modified": "2023-12-14T18:24:26.357179Z",
       "mimetype": null,
       "name": "auto_examples",
       "path": "auto_examples",
@@ -15,11 +15,11 @@
       "writable": true
     }
   ],
-  "created": "2023-12-14T17:33:29.259328Z",
+  "created": "2023-12-14T18:24:26.357179Z",
   "format": "json",
   "hash": null,
   "hash_algorithm": null,
-  "last_modified": "2023-12-14T17:33:29.259328Z",
+  "last_modified": "2023-12-14T18:24:26.357179Z",
   "mimetype": null,
   "name": "",
   "path": "",
diff --git a/dev/lite/api/contents/auto_examples/all.json b/dev/lite/api/contents/auto_examples/all.json
index 24ef06fb..8231bbf5 100644
--- a/dev/lite/api/contents/auto_examples/all.json
+++ b/dev/lite/api/contents/auto_examples/all.json
@@ -2,11 +2,11 @@
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-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.347319Z",
+      "last_modified": "2023-12-14T18:24:24.481175Z",
       "mimetype": null,
       "name": "01_encodings.ipynb",
       "path": "auto_examples/01_encodings.ipynb",
@@ -16,11 +16,11 @@
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     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.347319Z",
+      "last_modified": "2023-12-14T18:24:24.481175Z",
       "mimetype": null,
       "name": "02_feature_interpretation_with_gapencoder.ipynb",
       "path": "auto_examples/02_feature_interpretation_with_gapencoder.ipynb",
@@ -30,11 +30,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.347319Z",
+      "last_modified": "2023-12-14T18:24:24.481175Z",
       "mimetype": null,
       "name": "03_datetime_encoder.ipynb",
       "path": "auto_examples/03_datetime_encoder.ipynb",
@@ -44,11 +44,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.351319Z",
+      "last_modified": "2023-12-14T18:24:24.485175Z",
       "mimetype": null,
       "name": "04_fuzzy_joining.ipynb",
       "path": "auto_examples/04_fuzzy_joining.ipynb",
@@ -58,11 +58,11 @@
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     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
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       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.351319Z",
+      "last_modified": "2023-12-14T18:24:24.485175Z",
       "mimetype": null,
       "name": "05_deduplication.ipynb",
       "path": "auto_examples/05_deduplication.ipynb",
@@ -72,11 +72,11 @@
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     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.351319Z",
+      "last_modified": "2023-12-14T18:24:24.485175Z",
       "mimetype": null,
       "name": "06_ken_embeddings.ipynb",
       "path": "auto_examples/06_ken_embeddings.ipynb",
@@ -86,11 +86,11 @@
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     {
       "content": null,
-      "created": "2023-12-14T17:33:29.259328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.351319Z",
+      "last_modified": "2023-12-14T18:24:24.485175Z",
       "mimetype": null,
       "name": "07_multiple_key_join.ipynb",
       "path": "auto_examples/07_multiple_key_join.ipynb",
@@ -100,11 +100,11 @@
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     {
       "content": null,
-      "created": "2023-12-14T17:33:29.263328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.355319Z",
+      "last_modified": "2023-12-14T18:24:24.489175Z",
       "mimetype": null,
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@@ -114,11 +114,11 @@
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     {
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-      "created": "2023-12-14T17:33:29.263328Z",
+      "created": "2023-12-14T18:24:26.357179Z",
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       "hash_algorithm": null,
-      "last_modified": "2023-12-14T17:33:27.355319Z",
+      "last_modified": "2023-12-14T18:24:24.489175Z",
       "mimetype": null,
       "name": "09_interpolation_join.ipynb",
       "path": "auto_examples/09_interpolation_join.ipynb",
@@ -127,11 +127,11 @@
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     }
   ],
-  "created": "2023-12-14T17:33:29.263328Z",
+  "created": "2023-12-14T18:24:26.357179Z",
   "format": "json",
   "hash": null,
   "hash_algorithm": null,
-  "last_modified": "2023-12-14T17:33:29.263328Z",
+  "last_modified": "2023-12-14T18:24:26.357179Z",
   "mimetype": null,
   "name": "auto_examples",
   "path": "auto_examples",
diff --git a/dev/searchindex.js b/dev/searchindex.js
index 2ac79474..b46ab375 100644
--- a/dev/searchindex.js
+++ b/dev/searchindex.js
@@ -1 +1 @@
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reference", "Assembling: joining multiple tables", "Encoding: from a dataframe to a numerical matrix for machine learning", "Feature interpretation with the GapEncoder", "Handling datetime features with the DatetimeEncoder", "Fuzzy joining dirty tables with the Joiner", "Deduplicating misspelled categories", "Wikipedia embeddings to enrich the data", "Spatial join for flight data: Joining across multiple columns", "Self-aggregation on MovieLens", "Interpolation join: infer missing rows when joining two tables", "Examples", "Computation times", "Cleaning", "Development", "User guide", "Encoding: creating feature matrices", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.AggJoiner", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.AggTarget", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.DatetimeEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.DropCols", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.GapEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.InterpolationJoiner", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.Joiner", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.MinHashEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.SelectCols", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.SimilarityEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.TableVectorizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.DatasetAll", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.DatasetInfoOnly", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_drug_directory", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_employee_salaries", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_ken_embeddings", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_ken_table_aliases", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_ken_types", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_medical_charge", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_midwest_survey", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_movielens", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_open_payments", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_road_safety", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_traffic_violations", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.fetch_world_bank_indicator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub.datasets</span></code>.make_deduplication_data", "<code class=\"xref py py-mod docutils literal notranslate\"><span 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"reporting-bugs"]], "How do I submit a bug report?": [[1, "how-do-i-submit-a-bug-report"]], "Suggesting enhancements": [[1, "suggesting-enhancements"]], "How do I submit an enhancement proposal?": [[1, "how-do-i-submit-an-enhancement-proposal"]], "If the enhancement proposal is validated": [[1, "if-the-enhancement-proposal-is-validated"]], "If the enhancement is refused": [[1, "if-the-enhancement-is-refused"]], "Writing your first Pull Request": [[1, "writing-your-first-pull-request"]], "Preparing the ground": [[1, "preparing-the-ground"]], "Setting up the environment": [[1, "setting-up-the-environment"]], "Implementation": [[1, "implementation"]], "Submitting your code": [[1, "submitting-your-code"]], "Integration": [[1, "integration"]], "Continuous Integration (CI)": [[1, "continuous-integration-ci"]], "Building the documentation": [[1, "building-the-documentation"]], "Release process": [[2, "release-process"]], "Target audience": [[2, "target-audience"]], "Process": [[2, "process"]], "Resources": [[2, "resources"]], "About": [[3, "about"]], "API reference": [[4, "api-reference"]], "Assembling: joining multiple tables": [[5, "assembling-joining-multiple-tables"]], "Fuzzy joining tables": [[5, "fuzzy-joining-tables"]], "Joining external tables for machine learning": [[5, "joining-external-tables-for-machine-learning"]], "Column selection inside a pipeline": [[5, "column-selection-inside-a-pipeline"]], "Going further: embeddings for better analytics": [[5, "going-further-embeddings-for-better-analytics"]], "Encoding: from a dataframe to a numerical matrix for machine learning": [[6, "encoding-from-a-dataframe-to-a-numerical-matrix-for-machine-learning"]], "A simple prediction pipeline": [[6, "a-simple-prediction-pipeline"]], "Feature importances in the statistical model": [[6, "feature-importances-in-the-statistical-model"]], "Note:": [[6, null], [7, null], [9, null], [9, null], [10, null]], "Conclusion": [[6, "conclusion"], [7, "conclusion"], [8, 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skrub.datasets)": [[39, "skrub.datasets.fetch_medical_charge"]], "fetch_midwest_survey() (in module skrub.datasets)": [[40, "skrub.datasets.fetch_midwest_survey"]], "fetch_movielens() (in module skrub.datasets)": [[41, "skrub.datasets.fetch_movielens"]], "fetch_open_payments() (in module skrub.datasets)": [[42, "skrub.datasets.fetch_open_payments"]], "fetch_road_safety() (in module skrub.datasets)": [[43, "skrub.datasets.fetch_road_safety"]], "fetch_traffic_violations() (in module skrub.datasets)": [[44, "skrub.datasets.fetch_traffic_violations"]], "fetch_world_bank_indicator() (in module skrub.datasets)": [[45, "skrub.datasets.fetch_world_bank_indicator"]], "make_deduplication_data() (in module skrub.datasets)": [[46, "skrub.datasets.make_deduplication_data"]], "deduplicate() (in module skrub)": [[47, "skrub.deduplicate"]], "fuzzy_join() (in module skrub)": [[48, "skrub.fuzzy_join"]], "to_datetime() (in module skrub)": [[49, "skrub.to_datetime"]]}})
\ No newline at end of file
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"generated/skrub.datasets.fetch_drug_directory", "generated/skrub.datasets.fetch_employee_salaries", "generated/skrub.datasets.fetch_ken_embeddings", "generated/skrub.datasets.fetch_ken_table_aliases", "generated/skrub.datasets.fetch_ken_types", "generated/skrub.datasets.fetch_medical_charge", "generated/skrub.datasets.fetch_midwest_survey", "generated/skrub.datasets.fetch_movielens", "generated/skrub.datasets.fetch_open_payments", "generated/skrub.datasets.fetch_road_safety", "generated/skrub.datasets.fetch_traffic_violations", "generated/skrub.datasets.fetch_world_bank_indicator", "generated/skrub.datasets.make_deduplication_data", "generated/skrub.deduplicate", "generated/skrub.fuzzy_join", "generated/skrub.to_datetime", "includes/big_toc_css", "index", "install", "sg_execution_times", "unlisted", "vision"], "filenames": ["CHANGES.rst", "CONTRIBUTING.rst", "RELEASE_PROCESS.rst", "about.rst", "api.rst", "assembling.rst", "auto_examples/01_encodings.rst", "auto_examples/02_feature_interpretation_with_gapencoder.rst", "auto_examples/03_datetime_encoder.rst", "auto_examples/04_fuzzy_joining.rst", "auto_examples/05_deduplication.rst", "auto_examples/06_ken_embeddings.rst", "auto_examples/07_multiple_key_join.rst", "auto_examples/08_join_aggregation.rst", "auto_examples/09_interpolation_join.rst", "auto_examples/index.rst", "auto_examples/sg_execution_times.rst", "cleaning.rst", "development.rst", "documentation.rst", "encoding.rst", "generated/skrub.AggJoiner.rst", "generated/skrub.AggTarget.rst", "generated/skrub.DatetimeEncoder.rst", "generated/skrub.DropCols.rst", "generated/skrub.GapEncoder.rst", "generated/skrub.InterpolationJoiner.rst", "generated/skrub.Joiner.rst", "generated/skrub.MinHashEncoder.rst", "generated/skrub.SelectCols.rst", "generated/skrub.SimilarityEncoder.rst", "generated/skrub.TableVectorizer.rst", "generated/skrub.datasets.DatasetAll.rst", "generated/skrub.datasets.DatasetInfoOnly.rst", "generated/skrub.datasets.fetch_drug_directory.rst", "generated/skrub.datasets.fetch_employee_salaries.rst", "generated/skrub.datasets.fetch_ken_embeddings.rst", "generated/skrub.datasets.fetch_ken_table_aliases.rst", "generated/skrub.datasets.fetch_ken_types.rst", "generated/skrub.datasets.fetch_medical_charge.rst", "generated/skrub.datasets.fetch_midwest_survey.rst", "generated/skrub.datasets.fetch_movielens.rst", "generated/skrub.datasets.fetch_open_payments.rst", "generated/skrub.datasets.fetch_road_safety.rst", "generated/skrub.datasets.fetch_traffic_violations.rst", "generated/skrub.datasets.fetch_world_bank_indicator.rst", "generated/skrub.datasets.make_deduplication_data.rst", "generated/skrub.deduplicate.rst", "generated/skrub.fuzzy_join.rst", "generated/skrub.to_datetime.rst", "includes/big_toc_css.rst", "index.rst", "install.rst", "sg_execution_times.rst", "unlisted.rst", "vision.rst"], "titles": ["Changes", "Contributing to skrub", "Release process", "About", "API reference", "Assembling: joining multiple tables", "Encoding: from a dataframe to a numerical matrix for machine learning", "Feature interpretation with the GapEncoder", "Handling datetime features with the DatetimeEncoder", "Fuzzy joining dirty tables with the Joiner", "Deduplicating misspelled categories", "Wikipedia embeddings to enrich the data", "Spatial join for flight data: Joining across multiple columns", "Self-aggregation on MovieLens", "Interpolation join: infer missing rows when joining two tables", "Examples", "Computation times", "Cleaning", "Development", "User guide", "Encoding: creating feature matrices", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.AggJoiner", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.AggTarget", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">skrub</span></code>.DatetimeEncoder", "<code class=\"xref py 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skrub.datasets)": [[39, "skrub.datasets.fetch_medical_charge"]], "fetch_midwest_survey() (in module skrub.datasets)": [[40, "skrub.datasets.fetch_midwest_survey"]], "fetch_movielens() (in module skrub.datasets)": [[41, "skrub.datasets.fetch_movielens"]], "fetch_open_payments() (in module skrub.datasets)": [[42, "skrub.datasets.fetch_open_payments"]], "fetch_road_safety() (in module skrub.datasets)": [[43, "skrub.datasets.fetch_road_safety"]], "fetch_traffic_violations() (in module skrub.datasets)": [[44, "skrub.datasets.fetch_traffic_violations"]], "fetch_world_bank_indicator() (in module skrub.datasets)": [[45, "skrub.datasets.fetch_world_bank_indicator"]], "make_deduplication_data() (in module skrub.datasets)": [[46, "skrub.datasets.make_deduplication_data"]], "deduplicate() (in module skrub)": [[47, "skrub.deduplicate"]], "fuzzy_join() (in module skrub)": [[48, "skrub.fuzzy_join"]], "to_datetime() (in module skrub)": [[49, "skrub.to_datetime"]]}})
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diff --git a/dev/sg_execution_times.html b/dev/sg_execution_times.html
index 8465609d..e13cec18 100644
--- a/dev/sg_execution_times.html
+++ b/dev/sg_execution_times.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://skrub-data.github.io/stable/sg_execution_times.html" />
 <meta property="og:site_name" content="skrub" />
-<meta property="og:description" content="24:52.633 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns(../examples/07_multiple_key_join.py), 11:2..." />
+<meta property="og:description" content="23:31.339 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns(../examples/07_multiple_key_join.py), 10:5..." />
 <meta property="og:image" content="https://skrub-data.github.io/stable/_static/skrub.svg" />
 <meta property="og:image:alt" content="skrub" />
-<meta name="description" content="24:52.633 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns(../examples/07_multiple_key_join.py), 11:2..." />
+<meta name="description" content="23:31.339 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Spatial join for flight data: Joining across multiple columns(../examples/07_multiple_key_join.py), 10:5..." />
 
     <title>Computation times &#8212; skrub</title>
   
@@ -471,7 +471,7 @@
                   
   <section id="computation-times">
 <span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>24:52.633</strong> total execution time for 9 files <strong>from all galleries</strong>:</p>
+<p><strong>23:31.339</strong> total execution time for 9 files <strong>from all galleries</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -493,39 +493,39 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/07_multiple_key_join.html#sphx-glr-auto-examples-07-multiple-key-join-py"><span class="std std-ref">Spatial join for flight data: Joining across multiple columns</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_multiple_key_join.py</span></code>)</p></td>
-<td><p>11:29.384</p></td>
+<td><p>10:56.053</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/06_ken_embeddings.html#sphx-glr-auto-examples-06-ken-embeddings-py"><span class="std std-ref">Wikipedia embeddings to enrich the data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_ken_embeddings.py</span></code>)</p></td>
-<td><p>11:07.838</p></td>
+<td><p>10:30.856</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/01_encodings.html#sphx-glr-auto-examples-01-encodings-py"><span class="std std-ref">Encoding: from a dataframe to a numerical matrix for machine learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/01_encodings.py</span></code>)</p></td>
-<td><p>01:19.568</p></td>
+<td><p>01:08.670</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_fuzzy_joining.html#sphx-glr-auto-examples-04-fuzzy-joining-py"><span class="std std-ref">Fuzzy joining dirty tables with the Joiner</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_fuzzy_joining.py</span></code>)</p></td>
-<td><p>00:19.674</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/08_join_aggregation.html#sphx-glr-auto-examples-08-join-aggregation-py"><span class="std std-ref">Self-aggregation on MovieLens</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/08_join_aggregation.py</span></code>)</p></td>
+<td><p>00:21.351</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/08_join_aggregation.html#sphx-glr-auto-examples-08-join-aggregation-py"><span class="std std-ref">Self-aggregation on MovieLens</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/08_join_aggregation.py</span></code>)</p></td>
-<td><p>00:18.997</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_fuzzy_joining.html#sphx-glr-auto-examples-04-fuzzy-joining-py"><span class="std std-ref">Fuzzy joining dirty tables with the Joiner</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_fuzzy_joining.py</span></code>)</p></td>
+<td><p>00:17.135</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/09_interpolation_join.html#sphx-glr-auto-examples-09-interpolation-join-py"><span class="std std-ref">Interpolation join: infer missing rows when joining two tables</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/09_interpolation_join.py</span></code>)</p></td>
-<td><p>00:05.503</p></td>
+<td><p>00:06.568</p></td>
 <td><p>0.0</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_datetime_encoder.html#sphx-glr-auto-examples-03-datetime-encoder-py"><span class="std std-ref">Handling datetime features with the DatetimeEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_datetime_encoder.py</span></code>)</p></td>
-<td><p>00:04.948</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/05_deduplication.html#sphx-glr-auto-examples-05-deduplication-py"><span class="std std-ref">Deduplicating misspelled categories</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_deduplication.py</span></code>)</p></td>
+<td><p>00:04.909</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_deduplication.html#sphx-glr-auto-examples-05-deduplication-py"><span class="std std-ref">Deduplicating misspelled categories</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_deduplication.py</span></code>)</p></td>
-<td><p>00:04.680</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_datetime_encoder.html#sphx-glr-auto-examples-03-datetime-encoder-py"><span class="std std-ref">Handling datetime features with the DatetimeEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_datetime_encoder.py</span></code>)</p></td>
+<td><p>00:04.149</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/02_feature_interpretation_with_gapencoder.html#sphx-glr-auto-examples-02-feature-interpretation-with-gapencoder-py"><span class="std std-ref">Feature interpretation with the GapEncoder</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_feature_interpretation_with_gapencoder.py</span></code>)</p></td>
-<td><p>00:02.041</p></td>
+<td><p>00:01.648</p></td>
 <td><p>0.0</p></td>
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