apply corrections to docstrings

src/facet/inspection/_explainer.py

@@ -79,7 +79,7 @@ def make_explainer(
         """
         Construct a new :class:`~shap.Explainer` to compute shap values.
 
-        :param model: learner for which to compute shap values
+        :param model: fitted learner for which to compute shap values
         :param data: background dataset (optional)
         :return: the new explainer object
         """
@@ -147,13 +147,7 @@ def uses_background_dataset(self) -> bool:
     def make_explainer(
         self, model: LearnerDF, data: Optional[pd.DataFrame] = None
     ) -> Explainer:
-        """
-        Construct a new :class:`~shap.TreeExplainer` to compute shap values.
-
-        :param model: learner for which to compute shap values
-        :param data: background dataset (optional)
-        :return: the new explainer object
-        """
+        """[see superclass]"""
 
         self._validate_background_dataset(data=data)
 
@@ -176,7 +170,7 @@ def make_explainer(
         return explainer
 
 
-# @inheritdoc(match="[see superclass]")
+@inheritdoc(match="[see superclass]")
 class KernelExplainerFactory(ExplainerFactory):
     """
     A factory constructing class:`~shap.KernelExplainer` objects.
@@ -219,13 +213,7 @@ def uses_background_dataset(self) -> bool:
         return True
 
     def make_explainer(self, model: LearnerDF, data: pd.DataFrame) -> Explainer:
-        """
-        Construct a new :class:`~shap.KernelExplainer` to compute shap values.
-
-        :param model: learner for which to compute shap values
-        :param data: background dataset
-        :return: the new explainer object
-        """
+        """[see superclass]"""
 
         self._validate_background_dataset(data=data)
 

src/facet/selection/_selection.py

@@ -246,7 +246,7 @@ def __init__(
         :param scoring: a scorer to use when doing CV within GridSearch, defaults to \
             ``None``
         :param ranking_scorer: a function to calculate a scalar score for every \
-            crossfit, taking a :class:`.CrossfitScore` and returning a float. \
+            crossfit, taking a :class:`.CrossfitScores` and returning a float. \
             The resulting score is used to rank all crossfits (highest score is best). \
             Defaults to :meth:`.default_ranking_scorer`, calculating \
             `mean(scores) - 2 * std(scores)`.

src/facet/validation/_validation.py

@@ -81,7 +81,7 @@ def split(
         :param X: features
         :param y: target
         :param groups: not used
-        :return a generator yielding `(train, test)` tuples where train and test are \
+        :return: a generator yielding `(train, test)` tuples where train and test are \
             numpy arrays with train/test indices
         """
 
@@ -302,7 +302,7 @@ def split(
         :param X: features
         :param y: target
         :param groups: not used
-        :return a generator yielding `(train, test)` tuples where train and test are \
+        :return: a generator yielding `(train, test)` tuples where train and test are \
             numpy arrays with train/test indices
         """
         indices = np.arange(len(X))