diff --git a/LICENSE-3RD-PARTY.txt b/LICENSE-3RD-PARTY.txt
index 7d6a3aaa..3b0e2bee 100644
--- a/LICENSE-3RD-PARTY.txt
+++ b/LICENSE-3RD-PARTY.txt
@@ -119,6 +119,676 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 
+amalgam-lang
+6.0.5
+GNU Affero General Public License v3
+Howso Incorporated
+https://howso.com
+A direct interface with Amalgam compiled DLL or so.
+/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/amalgam_lang-6.0.5.dist-info/LICENSE.txt
+GNU AFFERO GENERAL PUBLIC LICENSE
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+<one line to give the program's name and a brief idea of what it does.>
+Copyright (C) <year>  <name of author>
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published
+by the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU Affero General Public License for more details.
+
+You should have received a copy of the GNU Affero General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<http://www.gnu.org/licenses/>.
+
+
 certifi
 2023.11.17
 Mozilla Public License 2.0 (MPL 2.0)
diff --git a/howso/client/base.py b/howso/client/base.py
index 1af9b73a..6696052a 100644
--- a/howso/client/base.py
+++ b/howso/client/base.py
@@ -101,7 +101,6 @@ def set_random_seed(self, trainee_id, seed):
     @abstractmethod
     def train(
         self, trainee_id, cases, features=None, *,
-        ablatement_params=None,
         accumulate_weight_feature=None,
         batch_size=None,
         derived_features=None,
@@ -287,11 +286,12 @@ def react_into_features(
         distance_contribution: Union[bool, str] = False,
         familiarity_conviction_addition: Union[bool, str] = False,
         familiarity_conviction_removal: Union[bool, str] = False,
+        features=None,
+        influence_weight_entropy: Union[bool, str] = False,
         p_value_of_addition: Union[bool, str] = False,
         p_value_of_removal: Union[bool, str] = False,
         similarity_conviction: Union[bool, str] = False,
         use_case_weights: Union[bool, str] = False,
-        features=None,
         weight_feature=None
     ):
         """Calculate conviction and other data for the specified feature(s)."""
@@ -408,6 +408,32 @@ def analyze(
     def auto_analyze(self, trainee_id):
         """Auto-analyze the trainee model."""
 
+    @abstractmethod
+    def set_auto_ablation_params(
+        self,
+        trainee_id,
+        auto_ablation_enabled=False,
+        *,
+        auto_ablation_weight_feature=".case_weight",
+        conviction_lower_threshold=None,
+        conviction_upper_threshold=None,
+        exact_prediction_features=None,
+        infleunce_weight_entropy_threshold=0.6,
+        minimum_model_size=1_000,
+        relative_prediction_threshold_map=None,
+        residual_prediction_features=None,
+        tolerance_prediction_threshold_map=None,
+        **kwargs
+    ):
+        """Set trainee parameters for auto ablation."""
+    
+    @abstractmethod
+    def get_auto_ablation_params(
+        self,
+        trainee_id
+    ):
+        """Get trainee parameters for auto ablation set by :meth:`set_auto_ablation_params`."""
+
     @abstractmethod
     def set_auto_analyze_params(
         self,
diff --git a/howso/direct/client.py b/howso/direct/client.py
index 6667ee03..b44675e0 100644
--- a/howso/direct/client.py
+++ b/howso/direct/client.py
@@ -1182,7 +1182,6 @@ def train(  # noqa: C901
         cases: Union[List[List[object]], DataFrame],
         features: Optional[Iterable[str]] = None,
         *,
-        ablatement_params: Optional[Dict[str, List[object]]] = None,
         accumulate_weight_feature: Optional[str] = None,
         batch_size: Optional[int] = None,
         derived_features: Optional[Iterable[str]] = None,
@@ -1211,20 +1210,6 @@ def train(  # noqa: C901
                    cases DataFrame.
                 c. You want to re-order the columns that are trained.
 
-        ablatement_params : dict of str to list of object, optional
-            Where keys are a feature name and values are threshold_type where
-            threshold_type is one of:
-
-                - ['exact']: Don't train if prediction matches exactly
-                - ['tolerance', MIN, MAX]: Don't train if ``prediction
-                  >= (case value - MIN) & prediction <= (case value + MAX)``
-                - ['relative', PERCENT]: Don't train if
-                  ``abs(prediction - case value) / prediction <= PERCENT``
-                - ['residual']: Don't train if
-                  ``abs(prediction - case value) <= feature residual``
-
-            >>> {'species': ['exact'], 'sepal_length': ['tolerance', 0.1, 0.25]}
-
         accumulate_weight_feature : str, optional
             Name of feature into which to accumulate neighbors'
             influences as weight for ablated cases. If unspecified, will not
@@ -1327,7 +1312,6 @@ def train(  # noqa: C901
                 end = progress.current_tick + batch_size
                 response = self.howso.train(
                     trainee_id,
-                    ablatement_params=ablatement_params,
                     accumulate_weight_feature=accumulate_weight_feature,
                     derived_features=derived_features,
                     features=features,
@@ -3427,15 +3411,16 @@ def react_into_features(
         self,
         trainee_id: str,
         *,
-        features: Optional[Iterable[str]] = None,
+        distance_contribution: Optional[Union[str, bool]] = False,
         familiarity_conviction_addition: Optional[Union[str, bool]] = False,
         familiarity_conviction_removal: Optional[Union[str, bool]] = False,
+        features: Optional[Iterable[str]] = None,
+        influence_weight_entropy: Union[bool, str] = False,
         p_value_of_addition: Optional[Union[str, bool]] = False,
         p_value_of_removal: Optional[Union[str, bool]] = False,
         similarity_conviction: Optional[Union[str, bool]] = False,
-        distance_contribution: Optional[Union[str, bool]] = False,
+        use_case_weights: bool = False,
         weight_feature: Optional[str] = None,
-        use_case_weights: bool = False
     ):
         """
         Calculate and cache conviction and other statistics.
@@ -3454,6 +3439,10 @@ def react_into_features(
             The name of the feature to store conviction of removal
             values. If set to True the values will be stored to the feature
             'familiarity_conviction_removal'.
+        influence_weight_entropy : bool or str, default False
+            The name of the feature to store influence weight entropy values in.
+            If set to True, the values will be stored in the feature
+            'influence_weight_entropy'.
         p_value_of_addition : bool or str, default False
             The name of the feature to store p value of addition
             values. If set to True the values will be stored to the feature
@@ -3486,6 +3475,7 @@ def react_into_features(
             features=features,
             familiarity_conviction_addition=familiarity_conviction_addition,
             familiarity_conviction_removal=familiarity_conviction_removal,
+            influence_weight_entropy=influence_weight_entropy,
             p_value_of_addition=p_value_of_addition,
             p_value_of_removal=p_value_of_removal,
             similarity_conviction=similarity_conviction,
@@ -5060,9 +5050,8 @@ def set_auto_analyze_params(  # noqa: C901
         if kwargs:
             warn_params = ', '.join(kwargs)
             warnings.warn(
-                f'The following auto analyze parameter(s) "{warn_params}" '
-                'are not officially supported by analyze and may or may not '
-                'have an effect.', UserWarning)
+                f'The following auto ablation parameter(s) "{warn_params}" '
+                'are not officially supported or may not have an effect.', UserWarning)
 
         self.howso.auto_analyze_params(
             trainee_id=trainee_id,
@@ -5074,6 +5063,88 @@ def set_auto_analyze_params(  # noqa: C901
             **kwargs
         )
         self._auto_persist_trainee(trainee_id)
+    
+    def get_auto_ablation_params(self, trainee_id: str):
+        """
+        Get parameters set by :meth:`set_auto_ablation_params`.
+        """
+        self._auto_resolve_trainee(trainee_id)
+        return self.howso.get_auto_ablation_params(trainee_id)
+    
+    def set_auto_ablation_params(
+        self,
+        trainee_id: str,
+        auto_ablation_enabled: bool = False,
+        *,
+        auto_ablation_weight_feature: str = ".case_weight",
+        conviction_lower_threshold: Optional[float] = None,
+        conviction_upper_threshold: Optional[float] = None,
+        exact_prediction_features: Optional[List[str]] = None,
+        influence_weight_entropy_threshold: float = 0.6,
+        minimum_model_size: int = 1_000,
+        relative_prediction_threshold_map: Optional[Dict[str, float]] = None,
+        residual_prediction_features: Optional[List[str]] = None,
+        tolerance_prediction_threshold_map: Optional[Dict[str, Tuple[float, float]]] = None,
+        **kwargs
+    ):
+        """
+        Set trainee parameters for auto ablation.
+
+        .. note::
+            Auto-ablation is experimental and the API may change without deprecation.
+
+        Parameters
+        ----------
+        trainee_id : str
+            The ID of the Trainee to set auto ablation parameters for.
+        auto_ablation_enabled : bool, default False
+            When True, the :meth:`train` method will ablate cases that meet the set criteria.
+        auto_ablation_weight_feature : str, default ".case_weight"
+            The weight feature that should be accumulated to when cases are ablated.
+        minimum_model_size : int, default 1,000
+            The threshold of the minimum number of cases at which the model should auto-ablate.
+        influence_weight_entropy_threshold : float, default 0.6
+            The influence weight entropy quantile that a case must be beneath in order to be trained.
+        exact_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if the prediction matches exactly.
+        residual_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= feature residual.
+        tolerance_prediction_threshold_map : Optional[Dict[str, Tuple[float, float]]], optional
+            For each of the features specified, will ablate a case if the prediction >= (case value - MIN)
+            and the prediction <= (case value + MAX).
+        relative_prediction_threshold_map : Optional[Dict[str, float]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= relative threshold
+        conviction_lower_threshold : Optional[float], optional
+            The conviction value above which cases will be ablated.
+        conviction_upper_threshold : Optional[float], optional
+            The conviction value below which cases will be ablated.
+        """
+        params = dict(
+            auto_ablation_enabled=auto_ablation_enabled,
+            auto_ablation_weight_feature=auto_ablation_weight_feature,
+            minimum_model_size=minimum_model_size,
+            influence_weight_entropy_threshold=influence_weight_entropy_threshold,
+            exact_prediction_features=exact_prediction_features,
+            residual_prediction_features=residual_prediction_features,
+            tolerance_prediction_threshold_map=tolerance_prediction_threshold_map,
+            relative_prediction_threshold_map=relative_prediction_threshold_map,
+            conviction_lower_threshold=conviction_lower_threshold,
+            conviction_upper_threshold=conviction_upper_threshold,
+        )
+        params.update(kwargs)
+        if kwargs:
+            warn_params = ", ".join(kwargs)
+            warnings.warn(
+                f'The following parameter(s) "{warn_params}" are '
+                'not officially supported by auto ablation and may or may not have an effect.',
+                UserWarning
+            )
+        self._auto_resolve_trainee(trainee_id)
+        self.howso.set_auto_ablation_params(
+            trainee_id, **params
+        )
 
     def optimize(self, *args, **kwargs):
         """
diff --git a/howso/direct/core.py b/howso/direct/core.py
index 2f3f2d18..b4c554f0 100644
--- a/howso/direct/core.py
+++ b/howso/direct/core.py
@@ -757,6 +757,81 @@ def get_num_training_cases(self, trainee_id: str) -> Dict:
         """
         return self._execute("get_num_training_cases", {"trainee": trainee_id})
 
+    def get_auto_ablation_params(self, trainee_id: str):
+        """
+        Get trainee parameters for auto ablation set by :meth:`set_auto_ablation_params`.
+        """
+        return self._execute(
+            "get_auto_ablation_params", {"trainee": trainee_id}
+        )
+    
+    def set_auto_ablation_params(
+        self,
+        trainee_id: str,
+        auto_ablation_enabled: bool = False,
+        *,
+        auto_ablation_weight_feature: str = ".case_weight",
+        conviction_lower_threshold: Optional[float] = None,
+        conviction_upper_threshold: Optional[float] = None,
+        exact_prediction_features: Optional[List[str]] = None,
+        influence_weight_entropy_threshold: float = 0.6,
+        minimum_model_size: int = 1_000,
+        relative_prediction_threshold_map: Optional[Dict[str, float]] = None,
+        residual_prediction_features: Optional[List[str]] = None,
+        tolerance_prediction_threshold_map: Optional[Dict[str, Tuple[float, float]]] = None,
+        **kwargs
+    ):
+        """
+        Set trainee parameters for auto ablation.
+
+        .. note::
+            Auto-ablation is experimental and the API may change without deprecation.
+
+        Parameters
+        ----------
+        trainee_id : str
+            The ID of the Trainee to set auto ablation parameters for.
+        auto_ablation_enabled : bool, default False
+            When True, the :meth:`train` method will ablate cases that meet the set criteria.
+        auto_ablation_weight_feature : str, default ".case_weight"
+            The weight feature that should be accumulated to when cases are ablated.
+        minimum_model_size : int, default 1,000
+            The threshold ofr the minimum number of cases at which the model should auto-ablate.
+        influence_weight_entropy_threshold : float, default 0.6
+            The influence weight entropy quantile that a case must be beneath in order to be trained.
+        exact_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if the prediction matches exactly.
+        residual_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= feature residual.
+        tolerance_prediction_threshold_map : Optional[Dict[str, Tuple[float, float]]], optional
+            For each of the features specified, will ablate a case if the prediction >= (case value - MIN)
+            and the prediction <= (case value + MAX).
+        relative_prediction_threshold_map : Optional[Dict[str, float]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= relative threshold
+        conviction_lower_threshold : Optional[float], optional
+            The conviction value above which cases will be ablated.
+        conviction_upper_threshold : Optional[float], optional
+            The conviction value below which cases will be ablated.
+        """
+        return self._execute(
+            "set_auto_ablation_params",
+            {
+                "trainee": trainee_id,
+                "auto_ablation_enabled": auto_ablation_enabled,
+                "auto_ablation_weight_feature": auto_ablation_weight_feature,
+                "minimum_model_size": minimum_model_size,
+                "influence_weight_entropy_threshold": influence_weight_entropy_threshold,
+                "exact_prediction_features": exact_prediction_features,
+                "residual_prediction_features": residual_prediction_features,
+                "tolerance_prediction_threshold_map": tolerance_prediction_threshold_map,
+                "relative_prediction_threshold_map": relative_prediction_threshold_map,
+                "conviction_lower_threshold": conviction_lower_threshold,
+                "conviction_upper_threshold": conviction_upper_threshold,
+            }
+        )
+
     def auto_analyze_params(
         self,
         trainee_id: str,
@@ -1050,7 +1125,6 @@ def train(
         input_cases: List[List[Any]],
         features: Optional[Iterable[str]] = None,
         *,
-        ablatement_params: Optional[Dict[str, List[Any]]] = None,
         accumulate_weight_feature: Optional[str] = None,
         derived_features: Optional[Iterable[str]] = None,
         input_is_substituted: bool = False,
@@ -1069,8 +1143,6 @@ def train(
             One or more cases to train into the model.
         features : iterable of str, optional
             An iterable of feature names corresponding to the input cases.
-        ablatement_params : dict of str to list of object, optional
-            Parameters describing how to ablate cases.
         accumulate_weight_feature : str, optional
             Name of feature into which to accumulate neighbors'
             influences as weight for ablated cases. If unspecified, will not
@@ -1099,13 +1171,12 @@ def train(
         return self._execute("train", {
             "trainee": trainee_id,
             "input_cases": input_cases,
-            "features": features,
+            "accumulate_weight_feature": accumulate_weight_feature,
             "derived_features": derived_features,
-            "session": session,
-            "ablatement_params": ablatement_params,
-            "series": series,
+            "features": features,
             "input_is_substituted": input_is_substituted,
-            "accumulate_weight_feature": accumulate_weight_feature,
+            "series": series,
+            "session": session,
             "train_weights_only": train_weights_only,
         })
 
@@ -1804,15 +1875,16 @@ def react_into_features(
         self,
         trainee_id: str,
         *,
-        features: Optional[Iterable[str]] = None,
+        distance_contribution: bool = False,
         familiarity_conviction_addition: bool = False,
         familiarity_conviction_removal: bool = False,
+        features: Optional[Iterable[str]] = None,
+        influence_weight_entropy: Union[bool, str] = False,
         p_value_of_addition: bool = False,
         p_value_of_removal: bool = False,
         similarity_conviction: bool = False,
-        distance_contribution: bool = False,
+        use_case_weights: bool = False,
         weight_feature: Optional[str] = None,
-        use_case_weights: bool = False
     ) -> None:
         """
         Calculate and cache conviction and other statistics.
@@ -1831,6 +1903,10 @@ def react_into_features(
             The name of the feature to store conviction of removal
             values. If set to True the values will be stored to the feature
             'familiarity_conviction_removal'.
+        influence_weight_entropy : bool or str, default False
+            The name of the feature to store influence weight entropy values in.
+            If set to True, the values will be stored in the feature
+            'influence_weight_entropy'.
         p_value_of_addition : bool or str, default False
             The name of the feature to store p value of addition
             values. If set to True the values will be stored to the feature
@@ -1859,6 +1935,7 @@ def react_into_features(
             "features": features,
             "familiarity_conviction_addition": familiarity_conviction_addition,
             "familiarity_conviction_removal": familiarity_conviction_removal,
+            "influence_weight_entropy": influence_weight_entropy,
             "p_value_of_addition": p_value_of_addition,
             "p_value_of_removal": p_value_of_removal,
             "similarity_conviction": similarity_conviction,
diff --git a/howso/engine/trainee.py b/howso/engine/trainee.py
index ca584e03..5843bfdc 100644
--- a/howso/engine/trainee.py
+++ b/howso/engine/trainee.py
@@ -716,16 +716,15 @@ def train(
         self,
         cases: Union[List[List[object]], "DataFrame"],
         *,
-        input_is_substituted: bool = False,
-        train_weights_only: bool = False,
-        validate: bool = True,
-        ablatement_params: Optional[Dict[str, List[object]]] = None,
         accumulate_weight_feature: Optional[str] = None,
         batch_size: Optional[int] = None,
         derived_features: Optional[Iterable[str]] = None,
         features: Optional[Iterable[str]] = None,
+        input_is_substituted: bool = False,
         progress_callback: Optional[Callable] = None,
         series: Optional[str] = None,
+        train_weights_only: bool = False,
+        validate: bool = True,
     ) -> None:
         """
         Train one or more cases into the trainee (model).
@@ -734,18 +733,6 @@ def train(
         ----------
         cases : list of list of object or pandas.DataFrame
             One or more cases to train into the model.
-        ablatement_params : dict [str, list of obj], optional
-            A dict of feature name to threshold type.
-            Valid thresholds include:
-
-                - ['exact']: Don't train if prediction matches exactly
-                - ['tolerance', MIN, MAX]: Don't train if ``prediction
-                  >= (case value - MIN) & prediction <= (case value + MAX)``
-                - ['relative', PERCENT]: Don't train if
-                  ``abs(prediction - case value) / prediction <= PERCENT``
-                - ['residual']: Don't train if
-                  ``abs(prediction - case value) <= feature residual``
-
         accumulate_weight_feature : str, default None
             Name of feature into which to accumulate neighbors'
             influences as weight for ablated cases. If unspecified, will not
@@ -803,7 +790,6 @@ def train(
         if isinstance(self.client, AbstractHowsoClient):
             self.client.train(
                 trainee_id=self.id,
-                ablatement_params=ablatement_params,
                 accumulate_weight_feature=accumulate_weight_feature,
                 batch_size=batch_size,
                 cases=cases,
@@ -962,6 +948,79 @@ def auto_analyze(self) -> None:
             self.client.auto_analyze(self.id)
         else:
             raise ValueError("Client must have the 'auto_analyze' method.")
+    
+    def get_auto_ablation_params(self):
+        """
+        Get trainee parameters for auto ablation set by :meth:`set_auto_ablation_params`.
+        """
+        if isinstance(self.client, AbstractHowsoClient):
+            return self.client.get_auto_ablation_params(self.id)
+        else:
+            raise ValueError("Client must have the 'get_auto_ablation_params' method.")
+    
+    def set_auto_ablation_params(
+        self,
+        auto_ablation_enabled: bool = False,
+        *,
+        auto_ablation_weight_feature: str = ".case_weight",
+        conviction_lower_threshold: Optional[float] = None,
+        conviction_upper_threshold: Optional[float] = None,
+        exact_prediction_features: Optional[List[str]] = None,
+        influence_weight_entropy_threshold: float = 0.6,
+        minimum_model_size: int = 1_000,
+        relative_prediction_threshold_map: Optional[Dict[str, float]] = None,
+        residual_prediction_features: Optional[List[str]] = None,
+        tolerance_prediction_threshold_map: Optional[Dict[str, Tuple[float, float]]] = None,
+        **kwargs
+    ):
+        """
+        Set trainee parameters for auto ablation.
+
+        .. note::
+            Auto-ablation is experimental and the API may change without deprecation.
+
+        Parameters
+        ----------
+        auto_ablation_enabled : bool, default False
+            When True, the :meth:`train` method will ablate cases that meet the set criteria.
+        auto_ablation_weight_feature : str, default ".case_weight"
+            The weight feature that should be accumulated to when cases are ablated.
+        minimum_model_size : int, default 1,000
+            The threshold ofr the minimum number of cases at which the model should auto-ablate.
+        influence_weight_entropy_threshold : float, default 0.6
+            The influence weight entropy quantile that a case must be beneath in order to be trained.
+        exact_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if the prediction matches exactly.
+        residual_prediction_features : Optional[List[str]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= feature residual.
+        tolerance_prediction_threshold_map : Optional[Dict[str, Tuple[float, float]]], optional
+            For each of the features specified, will ablate a case if the prediction >= (case value - MIN)
+            and the prediction <= (case value + MAX).
+        relative_prediction_threshold_map : Optional[Dict[str, float]], optional
+            For each of the features specified, will ablate a case if
+            abs(prediction - case value) / prediction <= relative threshold
+        conviction_lower_threshold : Optional[float], optional
+            The conviction value above which cases will be ablated.
+        conviction_upper_threshold : Optional[float], optional
+            The conviction value below which cases will be ablated.
+        """
+        if isinstance(self.client, AbstractHowsoClient):
+            self.client.set_auto_ablation_params(
+                trainee_id=self.id,
+                auto_ablation_enabled=auto_ablation_enabled,
+                auto_ablation_weight_feature=auto_ablation_weight_feature,
+                minimum_model_size=minimum_model_size,
+                influence_weight_entropy_threshold=influence_weight_entropy_threshold,
+                exact_prediction_features=exact_prediction_features,
+                residual_prediction_features=residual_prediction_features,
+                tolerance_prediction_threshold_map=tolerance_prediction_threshold_map,
+                relative_prediction_threshold_map=relative_prediction_threshold_map,
+                conviction_lower_threshold=conviction_lower_threshold,
+                conviction_upper_threshold=conviction_upper_threshold,
+            )
+        else:
+            raise ValueError("Client must have the 'set_auto_ablation_params' method.")
 
     def set_auto_analyze_params(
         self,
@@ -3005,11 +3064,12 @@ def react_into_features(
         distance_contribution: Union[str, bool] = False,
         familiarity_conviction_addition: Union[str, bool] = False,
         familiarity_conviction_removal: Union[str, bool] = False,
+        features: Optional[Iterable[str]] = None,
+        influence_weight_entropy: Union[str, bool] = False,
         p_value_of_addition: Union[str, bool] = False,
         p_value_of_removal: Union[str, bool] = False,
         similarity_conviction: Union[str, bool] = False,
         use_case_weights: bool = False,
-        features: Optional[Iterable[str]] = None,
         weight_feature: Optional[str] = None,
     ) -> None:
         """
@@ -3031,6 +3091,10 @@ def react_into_features(
             'familiarity_conviction_removal'.
         features : list of str, optional
             A list of features to calculate convictions.
+        influence_weight_entropy : bool or str, default False
+            The name of the feature to store influence weight entropy values in.
+            If set to True, the values will be stored in the feature
+            'influence_weight_entropy'.
         p_value_of_addition : bool or str, default False
             The name of the feature to store p value of addition
             values. If set to True the values will be stored to the feature
@@ -3060,6 +3124,7 @@ def react_into_features(
                 distance_contribution=distance_contribution,
                 familiarity_conviction_addition=familiarity_conviction_addition,
                 familiarity_conviction_removal=familiarity_conviction_removal,
+                influence_weight_entropy=influence_weight_entropy,
                 p_value_of_addition=p_value_of_addition,
                 p_value_of_removal=p_value_of_removal,
                 similarity_conviction=similarity_conviction,
diff --git a/howso/scikit/scikit.py b/howso/scikit/scikit.py
index 638c821f..3e7fc127 100644
--- a/howso/scikit/scikit.py
+++ b/howso/scikit/scikit.py
@@ -505,6 +505,7 @@ def react_into_features(
         distance_contribution=False,
         familiarity_conviction_addition=False,
         familiarity_conviction_removal=False,
+        influence_weight_entropy=False,
         p_value_of_addition=False,
         p_value_of_removal=False,
         similarity_conviction=False,
@@ -530,6 +531,10 @@ def react_into_features(
             The name of the feature to store conviction of removal values. If
             set to True the values will be stored to the feature
             'familiarity_conviction_removal'.
+        influence_weight_entropy : bool or str, default False
+            The name of the feature to store influence weight entropy values in.
+            If set to True, the values will be stored in the feature
+            'influence_weight_entropy'.
         p_value_of_addition : bool or str, default False
             The name of the feature to store p value of addition values. If set
             to True the values will be stored to the feature
@@ -559,6 +564,7 @@ def react_into_features(
             distance_contribution=distance_contribution,
             familiarity_conviction_addition=familiarity_conviction_addition,
             familiarity_conviction_removal=familiarity_conviction_removal,
+            influence_weight_entropy=influence_weight_entropy,
             p_value_of_addition=p_value_of_addition,
             p_value_of_removal=p_value_of_removal,
             similarity_conviction=similarity_conviction,