diff --git a/dev/.buildinfo b/dev/.buildinfo
index 5a3c0cb3e..065c464a4 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: 2477e942aab9c78438a49ee8fb3d50a8
+config: e111dff184e2451e0a92cf034a83814e
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_images/sphx_glr_01_encodings_001.png b/dev/_images/sphx_glr_01_encodings_001.png
index 0819b4e21..3a8528524 100644
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diff --git a/dev/_images/sphx_glr_01_encodings_thumb.png b/dev/_images/sphx_glr_01_encodings_thumb.png
index 96e985853..c7a1afc3c 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 e93efe16e..b63a7b4f5 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 c229641e3..cc108f1c5 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 5d6089a58..56e66bc7d 100644
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diff --git a/dev/_images/sphx_glr_09_interpolation_join_003.png b/dev/_images/sphx_glr_09_interpolation_join_003.png
index 5613c13c5..a3f4a4be6 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 3ccd47ec6..d59810f56 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 183e70f49..b3883f666 100644
--- a/dev/_sources/auto_examples/01_encodings.rst.txt
+++ b/dev/_sources/auto_examples/01_encodings.rst.txt
@@ -440,44 +440,45 @@ corresponding columns:
  .. code-block:: none
 
 
-    array(['supports, support, recruit', 'shelters, centers, sheriff',
-           'rockville, fallsgrove, downtown', 'animal, individual, virtual',
-           'engineering, mangement, budgeting', 'station, state, estate',
-           'facilities, maintenance, accounts', 'silver, spring, ride',
-           'behavioral, health, school', 'equipment, management, automotive',
-           'patrol, 4th, 6th', 'safety, collision, section',
-           'communications, communication, telecommunications',
-           'warehouse, delivery, operations', 'highway, welfare, services',
-           'gaithersburg, the, clarksburg', 'nicholson, transit, taxicab',
-           'development, planning, stormwater',
-           'assessment, protective, process',
-           'construction, instruction, building',
-           'mechanical, special, commercial',
-           'technology, systems, telephone', 'district, urban, 3rd',
-           'family, sexual, crimes', 'security, mccf, unit',
-           'custody, members, customer',
+    array(['services, highway, service', 'engineering, training, planning',
+           'traffic, safety, alcohol', 'sexual, family, crimes',
+           'district, payroll, squad', 'construction, instruction, building',
+           'silver, spring, ride', 'behavioral, health, school',
+           'delivery, operations, special', 'gaithersburg, clarksburg, the',
+           'welfare, childhood, children', 'toddlers, custody, members',
+           'supports, support, network', 'station, state, estate',
+           'director, officers, projects', 'management, fleet, parking',
+           'maintenance, facilities, eligibility',
+           'processing, programs, accounting', 'security, mc311, mccf',
+           'communications, communication, immunization',
+           'administration, battalion, administrative', 'patrol, 4th, 6th',
+           'procurement, protective, fiscal',
+           'development, residential, stormwater',
            'investigative, investigations, criminal',
-           'administration, administrative, battalion',
-           'assistance, eligibility, emergency', 'automated, traffic, office',
-           'master, registered, firefighter', 'manager, budget, projects',
-           'specialist, special, environmental', 'candidate, police, cadet',
-           'officer, office, traffic', 'operator, bus, operations',
-           'income, assistance, client', 'liquor, clerk, store',
-           'rescuer, recruit, firefighter', 'technician, mechanic, supply',
-           'program, programs, procurement', 'lieutenant, captain, chief',
-           'community, nurse, unit', 'school, health, room',
+           'emergency, centers, center', 'nicholson, transit, taxicab',
+           'automotive, assessment, equipment',
+           'technology, systems, telephone', 'rockville, twinbrook, library',
+           'dietary, security, partnerships',
+           'operator, equipment, apprentice',
+           'recreation, planning, renovation',
+           'master, registered, firefighter', 'candidate, police, of',
+           'officer, office, police', 'specialist, special, procurement',
+           'program, programs, projects', 'technician, mechanic, supply',
+           'legislative, principal, executive',
+           'coordinator, coordinating, depot', 'sergeant, cadet, police',
+           'recruit, firefighter, rescuer', 'community, health, nurse',
+           'warehouse, welfare, caseworker',
+           'enforcement, permitting, inspector', 'manager, budget, engineer',
+           'school, room, behavioral', 'captain, rescue, chief',
+           'supervisory, supervisor, therapist',
            'communications, telecommunications, safety',
-           'coordinator, services, service', 'accountant, assistant, county',
-           'supervisor, supervisory, sergeant',
-           'craftsworker, customer, public', 'sheriff, deputy, autobody',
-           'enforcement, permitting, inspector',
-           'correctional, correction, records',
-           'information, technology, recreation',
-           'administrative, principal, executive',
-           'crossing, purchasing, engineer', 'warehouse, welfare, driver',
-           'corporal, erp, behavioral', 'worker, social, leader',
-           'librarian, library, telephone',
-           'equipment, investment, investigator'], dtype=object)
+           'liquor, clerk, store', 'librarian, crossing, library',
+           'sheriff, deputy, autobody',
+           'information, technology, technologist',
+           'lieutenant, attendant, facilities', 'services, service, urban',
+           'assistance, assistant, income',
+           'correctional, correction, corporal',
+           'administrative, administration, administrator'], dtype=object)
 
 
 
@@ -559,7 +560,7 @@ Let's look at the cross-validated R2 score of our model:
 
  .. code-block:: none
 
-    R2 score:  mean: 0.922; std: 0.013
+    R2 score:  mean: 0.919; std: 0.017
 
 
 
@@ -695,7 +696,7 @@ to plot the feature importances.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 16.726 seconds)
+   **Total running time of the script:** (1 minutes 20.106 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 5244a0a35..b233532e7 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 1.903 seconds)
+   **Total running time of the script:** (0 minutes 1.818 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 106bb51e9..a3029da7e 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 4.509 seconds)
+   **Total running time of the script:** (0 minutes 4.276 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 bc8356af6..85eb82281 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 19.485 seconds)
+   **Total running time of the script:** (0 minutes 20.298 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 e1647d9a4..4b304247b 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 4.891 seconds)
+   **Total running time of the script:** (0 minutes 5.398 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 d7c5deb74..a6510c37a 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
 
 
-    {'games', 'all_entities', 'companies', 'movies', 'albums', 'schools'}
+    {'games', 'all_entities', 'movies', 'schools', 'companies', 'albums'}
 
 
 
@@ -840,7 +840,7 @@ It helped significantly improve the prediction score.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (11 minutes 15.721 seconds)
+   **Total running time of the script:** (11 minutes 31.752 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 c74538736..d88d0a20e 100644
--- a/dev/_sources/auto_examples/07_multiple_key_join.rst.txt
+++ b/dev/_sources/auto_examples/07_multiple_key_join.rst.txt
@@ -1225,7 +1225,7 @@ 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(
 
-    0.58915
+    0.58585
 
 
 
@@ -1243,7 +1243,7 @@ Our final cross-validated accuracy score is 0.58.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (10 minutes 53.468 seconds)
+   **Total running time of the script:** (12 minutes 0.348 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 035284f67..29a896783 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>0.045640</td>
-          <td>0.041599</td>
-          <td>0.064299</td>
-          <td>0.075415</td>
-          <td>0.082621</td>
+          <td>0.035662</td>
+          <td>0.053087</td>
+          <td>0.072071</td>
+          <td>0.066001</td>
+          <td>0.079685</td>
         </tr>
         <tr>
           <th>split2_test_score</th>
-          <td>0.071529</td>
-          <td>0.074321</td>
-          <td>0.089585</td>
-          <td>0.088302</td>
-          <td>0.092612</td>
+          <td>0.076336</td>
+          <td>0.082476</td>
+          <td>0.094569</td>
+          <td>0.091759</td>
+          <td>0.091283</td>
         </tr>
         <tr>
           <th>split3_test_score</th>
-          <td>0.040797</td>
-          <td>0.063690</td>
-          <td>0.069269</td>
-          <td>0.062119</td>
-          <td>0.069033</td>
+          <td>0.039651</td>
+          <td>0.067618</td>
+          <td>0.056371</td>
+          <td>0.073471</td>
+          <td>0.074189</td>
         </tr>
         <tr>
           <th>split4_test_score</th>
-          <td>0.138299</td>
-          <td>0.121267</td>
-          <td>0.140101</td>
-          <td>0.142540</td>
-          <td>0.140436</td>
+          <td>0.141117</td>
+          <td>0.125026</td>
+          <td>0.144522</td>
+          <td>0.144590</td>
+          <td>0.141754</td>
         </tr>
         <tr>
           <th>split5_test_score</th>
-          <td>0.106619</td>
-          <td>0.112459</td>
-          <td>0.104979</td>
-          <td>0.108407</td>
-          <td>0.114175</td>
+          <td>0.109087</td>
+          <td>0.110900</td>
+          <td>0.106453</td>
+          <td>0.109212</td>
+          <td>0.114790</td>
         </tr>
         <tr>
           <th>split6_test_score</th>
-          <td>0.077635</td>
-          <td>0.103037</td>
-          <td>0.107903</td>
-          <td>0.105183</td>
-          <td>0.106193</td>
+          <td>0.082496</td>
+          <td>0.102770</td>
+          <td>0.109770</td>
+          <td>0.110297</td>
+          <td>0.112940</td>
         </tr>
         <tr>
           <th>split7_test_score</th>
-          <td>0.074042</td>
-          <td>0.067023</td>
-          <td>0.070372</td>
-          <td>0.070241</td>
-          <td>0.076133</td>
+          <td>0.059398</td>
+          <td>0.066302</td>
+          <td>0.060979</td>
+          <td>0.064334</td>
+          <td>0.076729</td>
         </tr>
         <tr>
           <th>split8_test_score</th>
-          <td>0.110951</td>
-          <td>0.114420</td>
-          <td>0.117400</td>
-          <td>0.113873</td>
-          <td>0.132726</td>
+          <td>0.104940</td>
+          <td>0.111360</td>
+          <td>0.117624</td>
+          <td>0.120712</td>
+          <td>0.119874</td>
         </tr>
         <tr>
           <th>split9_test_score</th>
-          <td>0.121891</td>
-          <td>0.148754</td>
-          <td>0.175560</td>
-          <td>0.161689</td>
-          <td>0.180554</td>
+          <td>0.106619</td>
+          <td>0.155110</td>
+          <td>0.171715</td>
+          <td>0.164387</td>
+          <td>0.176994</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 16.742 seconds)
+   **Total running time of the script:** (0 minutes 17.713 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 e7bfe79de..a6cab4b84 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>34.805413</td>
-          <td>40.631545</td>
-          <td>0.060178</td>
+          <td>34.198740</td>
+          <td>142.770662</td>
+          <td>0.056624</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>26.568328</td>
-          <td>96.431877</td>
-          <td>0.331631</td>
+          <td>26.708394</td>
+          <td>71.834684</td>
+          <td>0.453572</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>31.457290</td>
-          <td>7.708034</td>
-          <td>0.060178</td>
+          <td>31.337142</td>
+          <td>53.627947</td>
+          <td>0.140611</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>34.110941</td>
-          <td>46.476880</td>
-          <td>0.060178</td>
+          <td>33.949103</td>
+          <td>93.586318</td>
+          <td>0.140611</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>29.202521</td>
-          <td>25.925854</td>
-          <td>0.272614</td>
+          <td>28.957713</td>
+          <td>30.326362</td>
+          <td>0.110605</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>-0.047682</td>
+          <td>2.084197</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>15.604202</td>
+          <td>13.988828</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>6.541363</td>
+          <td>7.269396</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>7.067635</td>
+          <td>7.983380</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>7.669651</td>
+          <td>7.760605</td>
         </tr>
       </tbody>
     </table>
@@ -678,11 +678,11 @@ States with the lowest average predicted temperatures: Alaska, Montana, North Da
 
 
     state
-    AK   -2.772777
-    MT    0.478902
-    WA    0.795014
-    ND    1.102758
-    MN    1.201596
+    AK   -3.617829
+    MT    0.039900
+    WA    0.606960
+    ND    1.314403
+    MN    1.495447
     Name: TMAX, dtype: float64
 
 
@@ -707,11 +707,11 @@ States with the highest predicted temperatures: Puerto Rico, Virgin Islands, Haw
 
 
     state
-    LA    21.597141
-    FL    24.702922
-    HI    26.811835
-    VI    29.999518
-    PR    30.403917
+    LA    21.518561
+    FL    24.747995
+    HI    26.399745
+    VI    30.839051
+    PR    31.368437
     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 5.022 seconds)
+   **Total running time of the script:** (0 minutes 5.384 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 0917dcd50..8fc95acb9 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
 =================
-**24:18.466** total execution time for 9 files **from auto_examples**:
+**25:47.091** total execution time for 9 files **from auto_examples**:
 
 .. container::
 
@@ -32,30 +32,30 @@ Computation times
    * - Example
      - Time
      - Mem (MB)
-   * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``06_ken_embeddings.py``)
-     - 11:15.721
-     - 0.0
    * - :ref:`sphx_glr_auto_examples_07_multiple_key_join.py` (``07_multiple_key_join.py``)
-     - 10:53.468
+     - 12:00.348
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``06_ken_embeddings.py``)
+     - 11:31.752
      - 0.0
    * - :ref:`sphx_glr_auto_examples_01_encodings.py` (``01_encodings.py``)
-     - 01:16.726
+     - 01:20.106
      - 0.0
    * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``04_fuzzy_joining.py``)
-     - 00:19.485
+     - 00:20.298
      - 0.0
    * - :ref:`sphx_glr_auto_examples_08_join_aggregation.py` (``08_join_aggregation.py``)
-     - 00:16.742
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``09_interpolation_join.py``)
-     - 00:05.022
+     - 00:17.713
      - 0.0
    * - :ref:`sphx_glr_auto_examples_05_deduplication.py` (``05_deduplication.py``)
-     - 00:04.891
+     - 00:05.398
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``09_interpolation_join.py``)
+     - 00:05.384
      - 0.0
    * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``03_datetime_encoder.py``)
-     - 00:04.509
+     - 00:04.276
      - 0.0
    * - :ref:`sphx_glr_auto_examples_02_feature_interpretation_with_gapencoder.py` (``02_feature_interpretation_with_gapencoder.py``)
-     - 00:01.903
+     - 00:01.818
      - 0.0
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index 78d2a6189..eab2c7d8f 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**24:18.466** total execution time for 9 files **from all galleries**:
+**25:47.091** total execution time for 9 files **from all galleries**:
 
 .. container::
 
@@ -32,30 +32,30 @@ Computation times
    * - Example
      - Time
      - Mem (MB)
-   * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``../examples/06_ken_embeddings.py``)
-     - 11:15.721
-     - 0.0
    * - :ref:`sphx_glr_auto_examples_07_multiple_key_join.py` (``../examples/07_multiple_key_join.py``)
-     - 10:53.468
+     - 12:00.348
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_06_ken_embeddings.py` (``../examples/06_ken_embeddings.py``)
+     - 11:31.752
      - 0.0
    * - :ref:`sphx_glr_auto_examples_01_encodings.py` (``../examples/01_encodings.py``)
-     - 01:16.726
+     - 01:20.106
      - 0.0
    * - :ref:`sphx_glr_auto_examples_04_fuzzy_joining.py` (``../examples/04_fuzzy_joining.py``)
-     - 00:19.485
+     - 00:20.298
      - 0.0
    * - :ref:`sphx_glr_auto_examples_08_join_aggregation.py` (``../examples/08_join_aggregation.py``)
-     - 00:16.742
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``../examples/09_interpolation_join.py``)
-     - 00:05.022
+     - 00:17.713
      - 0.0
    * - :ref:`sphx_glr_auto_examples_05_deduplication.py` (``../examples/05_deduplication.py``)
-     - 00:04.891
+     - 00:05.398
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_09_interpolation_join.py` (``../examples/09_interpolation_join.py``)
+     - 00:05.384
      - 0.0
    * - :ref:`sphx_glr_auto_examples_03_datetime_encoder.py` (``../examples/03_datetime_encoder.py``)
-     - 00:04.509
+     - 00:04.276
      - 0.0
    * - :ref:`sphx_glr_auto_examples_02_feature_interpretation_with_gapencoder.py` (``../examples/02_feature_interpretation_with_gapencoder.py``)
-     - 00:01.903
+     - 00:01.818
      - 0.0
diff --git a/dev/auto_examples/01_encodings.html b/dev/auto_examples/01_encodings.html
index 0417b4cff..ff6f8be96 100644
--- a/dev/auto_examples/01_encodings.html
+++ b/dev/auto_examples/01_encodings.html
@@ -797,44 +797,45 @@ <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;supports, support, recruit&#39;, &#39;shelters, centers, sheriff&#39;,
-       &#39;rockville, fallsgrove, downtown&#39;, &#39;animal, individual, virtual&#39;,
-       &#39;engineering, mangement, budgeting&#39;, &#39;station, state, estate&#39;,
-       &#39;facilities, maintenance, accounts&#39;, &#39;silver, spring, ride&#39;,
-       &#39;behavioral, health, school&#39;, &#39;equipment, management, automotive&#39;,
-       &#39;patrol, 4th, 6th&#39;, &#39;safety, collision, section&#39;,
-       &#39;communications, communication, telecommunications&#39;,
-       &#39;warehouse, delivery, operations&#39;, &#39;highway, welfare, services&#39;,
-       &#39;gaithersburg, the, clarksburg&#39;, &#39;nicholson, transit, taxicab&#39;,
-       &#39;development, planning, stormwater&#39;,
-       &#39;assessment, protective, process&#39;,
-       &#39;construction, instruction, building&#39;,
-       &#39;mechanical, special, commercial&#39;,
-       &#39;technology, systems, telephone&#39;, &#39;district, urban, 3rd&#39;,
-       &#39;family, sexual, crimes&#39;, &#39;security, mccf, unit&#39;,
-       &#39;custody, members, customer&#39;,
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([&#39;services, highway, service&#39;, &#39;engineering, training, planning&#39;,
+       &#39;traffic, safety, alcohol&#39;, &#39;sexual, family, crimes&#39;,
+       &#39;district, payroll, squad&#39;, &#39;construction, instruction, building&#39;,
+       &#39;silver, spring, ride&#39;, &#39;behavioral, health, school&#39;,
+       &#39;delivery, operations, special&#39;, &#39;gaithersburg, clarksburg, the&#39;,
+       &#39;welfare, childhood, children&#39;, &#39;toddlers, custody, members&#39;,
+       &#39;supports, support, network&#39;, &#39;station, state, estate&#39;,
+       &#39;director, officers, projects&#39;, &#39;management, fleet, parking&#39;,
+       &#39;maintenance, facilities, eligibility&#39;,
+       &#39;processing, programs, accounting&#39;, &#39;security, mc311, mccf&#39;,
+       &#39;communications, communication, immunization&#39;,
+       &#39;administration, battalion, administrative&#39;, &#39;patrol, 4th, 6th&#39;,
+       &#39;procurement, protective, fiscal&#39;,
+       &#39;development, residential, stormwater&#39;,
        &#39;investigative, investigations, criminal&#39;,
-       &#39;administration, administrative, battalion&#39;,
-       &#39;assistance, eligibility, emergency&#39;, &#39;automated, traffic, office&#39;,
-       &#39;master, registered, firefighter&#39;, &#39;manager, budget, projects&#39;,
-       &#39;specialist, special, environmental&#39;, &#39;candidate, police, cadet&#39;,
-       &#39;officer, office, traffic&#39;, &#39;operator, bus, operations&#39;,
-       &#39;income, assistance, client&#39;, &#39;liquor, clerk, store&#39;,
-       &#39;rescuer, recruit, firefighter&#39;, &#39;technician, mechanic, supply&#39;,
-       &#39;program, programs, procurement&#39;, &#39;lieutenant, captain, chief&#39;,
-       &#39;community, nurse, unit&#39;, &#39;school, health, room&#39;,
+       &#39;emergency, centers, center&#39;, &#39;nicholson, transit, taxicab&#39;,
+       &#39;automotive, assessment, equipment&#39;,
+       &#39;technology, systems, telephone&#39;, &#39;rockville, twinbrook, library&#39;,
+       &#39;dietary, security, partnerships&#39;,
+       &#39;operator, equipment, apprentice&#39;,
+       &#39;recreation, planning, renovation&#39;,
+       &#39;master, registered, firefighter&#39;, &#39;candidate, police, of&#39;,
+       &#39;officer, office, police&#39;, &#39;specialist, special, procurement&#39;,
+       &#39;program, programs, projects&#39;, &#39;technician, mechanic, supply&#39;,
+       &#39;legislative, principal, executive&#39;,
+       &#39;coordinator, coordinating, depot&#39;, &#39;sergeant, cadet, police&#39;,
+       &#39;recruit, firefighter, rescuer&#39;, &#39;community, health, nurse&#39;,
+       &#39;warehouse, welfare, caseworker&#39;,
+       &#39;enforcement, permitting, inspector&#39;, &#39;manager, budget, engineer&#39;,
+       &#39;school, room, behavioral&#39;, &#39;captain, rescue, chief&#39;,
+       &#39;supervisory, supervisor, therapist&#39;,
        &#39;communications, telecommunications, safety&#39;,
-       &#39;coordinator, services, service&#39;, &#39;accountant, assistant, county&#39;,
-       &#39;supervisor, supervisory, sergeant&#39;,
-       &#39;craftsworker, customer, public&#39;, &#39;sheriff, deputy, autobody&#39;,
-       &#39;enforcement, permitting, inspector&#39;,
-       &#39;correctional, correction, records&#39;,
-       &#39;information, technology, recreation&#39;,
-       &#39;administrative, principal, executive&#39;,
-       &#39;crossing, purchasing, engineer&#39;, &#39;warehouse, welfare, driver&#39;,
-       &#39;corporal, erp, behavioral&#39;, &#39;worker, social, leader&#39;,
-       &#39;librarian, library, telephone&#39;,
-       &#39;equipment, investment, investigator&#39;], dtype=object)
+       &#39;liquor, clerk, store&#39;, &#39;librarian, crossing, library&#39;,
+       &#39;sheriff, deputy, autobody&#39;,
+       &#39;information, technology, technologist&#39;,
+       &#39;lieutenant, attendant, facilities&#39;, &#39;services, service, urban&#39;,
+       &#39;assistance, assistant, income&#39;,
+       &#39;correctional, correction, corporal&#39;,
+       &#39;administrative, administration, administrator&#39;], dtype=object)
 </pre></div>
 </div>
 <ul class="simple">
@@ -867,7 +868,7 @@ <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: 0.922; std: 0.013
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>R2 score:  mean: 0.919; std: 0.017
 </pre></div>
 </div>
 <p>The simple pipeline applied on this complex dataset gave us very good results.</p>
@@ -936,7 +937,7 @@ <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 16.726 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 20.106 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/0.5.0?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 31ffc58cb..1da51b6cd 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 1.903 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.818 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/0.5.0?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 67e427a80..8aa6a1167 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 4.509 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.276 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/0.5.0?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 09794401e..e496b4d9f 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 19.485 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 20.298 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/0.5.0?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 836077aec..81e856b7d 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 4.891 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.398 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/0.5.0?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 7130dc267..851429ac3 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;games&#39;, &#39;all_entities&#39;, &#39;companies&#39;, &#39;movies&#39;, &#39;albums&#39;, &#39;schools&#39;}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;games&#39;, &#39;all_entities&#39;, &#39;movies&#39;, &#39;schools&#39;, &#39;companies&#39;, &#39;albums&#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 15.721 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (11 minutes 31.752 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/0.5.0?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 94a602385..876b1dc0e 100644
--- a/dev/auto_examples/07_multiple_key_join.html
+++ b/dev/auto_examples/07_multiple_key_join.html
@@ -1513,7 +1513,7 @@ <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(
 
-0.58915
+0.58585
 </pre></div>
 </div>
 </section>
@@ -1523,7 +1523,7 @@ <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> (10 minutes 53.468 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (12 minutes 0.348 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-07-multiple-key-join-py">
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diff --git a/dev/auto_examples/08_join_aggregation.html b/dev/auto_examples/08_join_aggregation.html
index 85973a309..7488bf7f6 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>0.045640</td>
-      <td>0.041599</td>
-      <td>0.064299</td>
-      <td>0.075415</td>
-      <td>0.082621</td>
+      <td>0.035662</td>
+      <td>0.053087</td>
+      <td>0.072071</td>
+      <td>0.066001</td>
+      <td>0.079685</td>
     </tr>
     <tr>
       <th>split2_test_score</th>
-      <td>0.071529</td>
-      <td>0.074321</td>
-      <td>0.089585</td>
-      <td>0.088302</td>
-      <td>0.092612</td>
+      <td>0.076336</td>
+      <td>0.082476</td>
+      <td>0.094569</td>
+      <td>0.091759</td>
+      <td>0.091283</td>
     </tr>
     <tr>
       <th>split3_test_score</th>
-      <td>0.040797</td>
-      <td>0.063690</td>
-      <td>0.069269</td>
-      <td>0.062119</td>
-      <td>0.069033</td>
+      <td>0.039651</td>
+      <td>0.067618</td>
+      <td>0.056371</td>
+      <td>0.073471</td>
+      <td>0.074189</td>
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     <tr>
       <th>split4_test_score</th>
-      <td>0.138299</td>
-      <td>0.121267</td>
-      <td>0.140101</td>
-      <td>0.142540</td>
-      <td>0.140436</td>
+      <td>0.141117</td>
+      <td>0.125026</td>
+      <td>0.144522</td>
+      <td>0.144590</td>
+      <td>0.141754</td>
     </tr>
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       <th>split5_test_score</th>
-      <td>0.106619</td>
-      <td>0.112459</td>
-      <td>0.104979</td>
-      <td>0.108407</td>
-      <td>0.114175</td>
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+      <td>0.110900</td>
+      <td>0.106453</td>
+      <td>0.109212</td>
+      <td>0.114790</td>
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       <th>split6_test_score</th>
-      <td>0.077635</td>
-      <td>0.103037</td>
-      <td>0.107903</td>
-      <td>0.105183</td>
-      <td>0.106193</td>
+      <td>0.082496</td>
+      <td>0.102770</td>
+      <td>0.109770</td>
+      <td>0.110297</td>
+      <td>0.112940</td>
     </tr>
     <tr>
       <th>split7_test_score</th>
-      <td>0.074042</td>
-      <td>0.067023</td>
-      <td>0.070372</td>
-      <td>0.070241</td>
-      <td>0.076133</td>
+      <td>0.059398</td>
+      <td>0.066302</td>
+      <td>0.060979</td>
+      <td>0.064334</td>
+      <td>0.076729</td>
     </tr>
     <tr>
       <th>split8_test_score</th>
-      <td>0.110951</td>
-      <td>0.114420</td>
-      <td>0.117400</td>
-      <td>0.113873</td>
-      <td>0.132726</td>
+      <td>0.104940</td>
+      <td>0.111360</td>
+      <td>0.117624</td>
+      <td>0.120712</td>
+      <td>0.119874</td>
     </tr>
     <tr>
       <th>split9_test_score</th>
-      <td>0.121891</td>
-      <td>0.148754</td>
-      <td>0.175560</td>
-      <td>0.161689</td>
-      <td>0.180554</td>
+      <td>0.106619</td>
+      <td>0.155110</td>
+      <td>0.171715</td>
+      <td>0.164387</td>
+      <td>0.176994</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>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.713 seconds)</p>
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/skrub-data/skrub/0.5.0?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 7dc3c0fb7..72c082a60 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>34.805413</td>
-      <td>40.631545</td>
-      <td>0.060178</td>
+      <td>34.198740</td>
+      <td>142.770662</td>
+      <td>0.056624</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>26.568328</td>
-      <td>96.431877</td>
-      <td>0.331631</td>
+      <td>26.708394</td>
+      <td>71.834684</td>
+      <td>0.453572</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>31.457290</td>
-      <td>7.708034</td>
-      <td>0.060178</td>
+      <td>31.337142</td>
+      <td>53.627947</td>
+      <td>0.140611</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>34.110941</td>
-      <td>46.476880</td>
-      <td>0.060178</td>
+      <td>33.949103</td>
+      <td>93.586318</td>
+      <td>0.140611</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>29.202521</td>
-      <td>25.925854</td>
-      <td>0.272614</td>
+      <td>28.957713</td>
+      <td>30.326362</td>
+      <td>0.110605</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>-0.047682</td>
+      <td>2.084197</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>15.604202</td>
+      <td>13.988828</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>6.541363</td>
+      <td>7.269396</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>7.067635</td>
+      <td>7.983380</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>7.669651</td>
+      <td>7.760605</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   -2.772777
-MT    0.478902
-WA    0.795014
-ND    1.102758
-MN    1.201596
+AK   -3.617829
+MT    0.039900
+WA    0.606960
+ND    1.314403
+MN    1.495447
 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    21.597141
-FL    24.702922
-HI    26.811835
-VI    29.999518
-PR    30.403917
+LA    21.518561
+FL    24.747995
+HI    26.399745
+VI    30.839051
+PR    31.368437
 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 5.022 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.384 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/0.5.0?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 52c23e910..ae0825425 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" />
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-<meta property="og:description" content="24:18.466 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Wikipedia embeddings to enrich the data( 06_ken_embeddings.py), 11:15.721, 0.0,, Spatial join for flight..." />
+<meta property="og:description" content="25:47.091 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), 12:00.348, 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="24:18.466 total execution time for 9 files from auto_examples:,,, Example, Time, Mem (MB),,, Wikipedia embeddings to enrich the data( 06_ken_embeddings.py), 11:15.721, 0.0,, Spatial join for flight..." />
+<meta name="description" content="25:47.091 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), 12:00.348, 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>24:18.466</strong> total execution time for 9 files <strong>from auto_examples</strong>:</p>
+<p><strong>25:47.091</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" />
@@ -492,40 +492,40 @@
 </tr>
 </thead>
 <tbody>
-<tr class="row-even"><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>11:15.721</p></td>
+<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>12:00.348</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><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>10:53.468</p></td>
+<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>11:31.752</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>01:16.726</p></td>
+<td><p>01:20.106</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>00:19.485</p></td>
+<td><p>00:20.298</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>00:16.742</p></td>
+<td><p>00:17.713</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>00:05.022</p></td>
+<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>00:05.398</p></td>
 <td><p>0.0</p></td>
 </tr>
-<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>00:04.891</p></td>
+<tr class="row-even"><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>00:05.384</p></td>
 <td><p>0.0</p></td>
 </tr>
 <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>00:04.509</p></td>
+<td><p>00:04.276</p></td>
 <td><p>0.0</p></td>
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 <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>00:01.903</p></td>
+<td><p>00:01.818</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 bb0c4402b..85427dbb4 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 8fccdbb1c..09671f62f 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 5bac72974..ff490cb1f 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 2c0bce6c9..4f3ea5d47 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 973cea9a6..b6191c70d 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 42830d824..b234c3b9f 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 d88974db5..7f1ac3e0d 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 43c81b130..d7f69ff1b 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 d825e4e52..97bc5319e 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 5d82c28b4..dcdbbf50b 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 c0d32e5ce..d2a72bb37 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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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/8c947b1/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/12216ec/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 ddc8c1fdd..c43032c20 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/8c947b1/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/12216ec/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 ad79df4b9..fa95a10bf 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/8c947b1/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/12216ec/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 c9522cfc3..cf26a4406 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/8c947b1/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/12216ec/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 c62590f30..09f3aa54a 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/8c947b1/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/12216ec/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 b0fc647d7..498a3ce4d 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/8c947b1/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/12216ec/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 1da0d1bc6..0959c6fca 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/8c947b1/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/12216ec/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 70667b169..2c737878d 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/8c947b1/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/12216ec/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 023f14607..537477d69 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/8c947b1/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/12216ec/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 0d75237d6..ad7587d21 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/8c947b1/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/12216ec/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 9dd367d09..3a493aed9 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/8c947b1/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/12216ec/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 2c1cb4563..1c5e07876 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/8c947b1/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/12216ec/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 0ce515ce4..ac8b7c32a 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/8c947b1/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/12216ec/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 a578830eb..58e4c43eb 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/8c947b1/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/12216ec/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 b0416d992..a8b155243 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/8c947b1/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/12216ec/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 4f1fb5458..f837b483e 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/8c947b1/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/12216ec/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 89d791303..310277ffe 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/8c947b1/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/12216ec/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 a6655c7fb..0f364c8bf 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/8c947b1/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/12216ec/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 310492521..acc07a735 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/8c947b1/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/12216ec/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/lite/api/contents/all.json b/dev/lite/api/contents/all.json
index 965d90b1a..3738f7912 100644
--- a/dev/lite/api/contents/all.json
+++ b/dev/lite/api/contents/all.json
@@ -2,11 +2,11 @@
   "content": [
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.055707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:54.055707Z",
+      "last_modified": "2023-12-12T15:57:12.130168Z",
       "mimetype": null,
       "name": "auto_examples",
       "path": "auto_examples",
@@ -15,11 +15,11 @@
       "writable": true
     }
   ],
-  "created": "2023-12-12T15:50:54.051707Z",
+  "created": "2023-12-12T15:57:12.130168Z",
   "format": "json",
   "hash": null,
   "hash_algorithm": null,
-  "last_modified": "2023-12-12T15:50:54.051707Z",
+  "last_modified": "2023-12-12T15:57:12.130168Z",
   "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 6a4eb61ea..ef8419287 100644
--- a/dev/lite/api/contents/auto_examples/all.json
+++ b/dev/lite/api/contents/auto_examples/all.json
@@ -2,11 +2,11 @@
   "content": [
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.299696Z",
+      "last_modified": "2023-12-12T15:57:10.222164Z",
       "mimetype": null,
       "name": "01_encodings.ipynb",
       "path": "auto_examples/01_encodings.ipynb",
@@ -16,11 +16,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.299696Z",
+      "last_modified": "2023-12-12T15:57:10.222164Z",
       "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": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.299696Z",
+      "last_modified": "2023-12-12T15:57:10.222164Z",
       "mimetype": null,
       "name": "03_datetime_encoder.ipynb",
       "path": "auto_examples/03_datetime_encoder.ipynb",
@@ -44,11 +44,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.299696Z",
+      "last_modified": "2023-12-12T15:57:10.222164Z",
       "mimetype": null,
       "name": "04_fuzzy_joining.ipynb",
       "path": "auto_examples/04_fuzzy_joining.ipynb",
@@ -58,11 +58,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.303696Z",
+      "last_modified": "2023-12-12T15:57:10.222164Z",
       "mimetype": null,
       "name": "05_deduplication.ipynb",
       "path": "auto_examples/05_deduplication.ipynb",
@@ -72,11 +72,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.303696Z",
+      "last_modified": "2023-12-12T15:57:10.226164Z",
       "mimetype": null,
       "name": "06_ken_embeddings.ipynb",
       "path": "auto_examples/06_ken_embeddings.ipynb",
@@ -86,11 +86,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.051707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.303696Z",
+      "last_modified": "2023-12-12T15:57:10.226164Z",
       "mimetype": null,
       "name": "07_multiple_key_join.ipynb",
       "path": "auto_examples/07_multiple_key_join.ipynb",
@@ -100,11 +100,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.055707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.303696Z",
+      "last_modified": "2023-12-12T15:57:10.226164Z",
       "mimetype": null,
       "name": "08_join_aggregation.ipynb",
       "path": "auto_examples/08_join_aggregation.ipynb",
@@ -114,11 +114,11 @@
     },
     {
       "content": null,
-      "created": "2023-12-12T15:50:54.055707Z",
+      "created": "2023-12-12T15:57:12.130168Z",
       "format": null,
       "hash": null,
       "hash_algorithm": null,
-      "last_modified": "2023-12-12T15:50:52.303696Z",
+      "last_modified": "2023-12-12T15:57:10.226164Z",
       "mimetype": null,
       "name": "09_interpolation_join.ipynb",
       "path": "auto_examples/09_interpolation_join.ipynb",
@@ -127,11 +127,11 @@
       "writable": true
     }
   ],
-  "created": "2023-12-12T15:50:54.055707Z",
+  "created": "2023-12-12T15:57:12.130168Z",
   "format": "json",
   "hash": null,
   "hash_algorithm": null,
-  "last_modified": "2023-12-12T15:50:54.055707Z",
+  "last_modified": "2023-12-12T15:57:12.130168Z",
   "mimetype": null,
   "name": "auto_examples",
   "path": "auto_examples",
diff --git a/dev/searchindex.js b/dev/searchindex.js
index c043d29cb..c7e0ef9ad 100644
--- a/dev/searchindex.js
+++ b/dev/searchindex.js
@@ -1 +1 @@
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"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 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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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"]]}})
\ No newline at end of file
diff --git a/dev/sg_execution_times.html b/dev/sg_execution_times.html
index bc42bfbc2..d53f5fb03 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="24:18.466 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Wikipedia embeddings to enrich the data(../examples/06_ken_embeddings.py), 11:15.721, 0.0,, Spatial join..." />
+<meta property="og:description" content="25:47.091 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), 12:0..." />
 <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="24:18.466 total execution time for 9 files from all galleries:,,, Example, Time, Mem (MB),,, Wikipedia embeddings to enrich the data(../examples/06_ken_embeddings.py), 11:15.721, 0.0,, Spatial join..." />
+<meta name="description" content="25:47.091 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), 12:0..." />
 
     <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>24:18.466</strong> total execution time for 9 files <strong>from all galleries</strong>:</p>
+<p><strong>25:47.091</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" />
@@ -492,40 +492,40 @@
 </tr>
 </thead>
 <tbody>
-<tr class="row-even"><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>11:15.721</p></td>
+<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>12:00.348</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><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>10:53.468</p></td>
+<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>11:31.752</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>01:16.726</p></td>
+<td><p>01:20.106</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>00:19.485</p></td>
+<td><p>00:20.298</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>00:16.742</p></td>
+<td><p>00:17.713</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>00:05.022</p></td>
+<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>00:05.398</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/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>00:04.891</p></td>
+<tr class="row-even"><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>00:05.384</p></td>
 <td><p>0.0</p></td>
 </tr>
 <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>00:04.509</p></td>
+<td><p>00:04.276</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>00:01.903</p></td>
+<td><p>00:01.818</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>