fix doc

Author: xadupre

CHANGELOGS.rst

@@ -6,6 +6,7 @@ Change Logs
 0.5.0
 =====
 
+* :pr:`118` major refactoring, changes CI, builds against scikit-learn 1.3 
 * :pr:`115` Updates tree decision criterion for scikit-learn 1.2 (2023-07-02)
 * :pr:`113` Removes normalize attributes (deprecated) (2022-11-29)
 * :pr:`110` Fixes perplexity issue with PredictableTSNE (2022-08-06)

_doc/index.rst

@@ -68,7 +68,7 @@ which trains a multi-layer perceptron with :epkg:`L1` norm...
     :caption: More
 
     license
-    README
+    CHANGELOGS
 
 Short example:
 

mlinsights/mlmodel/target_predictors.py

@@ -30,14 +30,11 @@ class TransformedTargetRegressor2(BaseEstimator, RegressorMixin):
     Useful for applying a non-linear transformation in regression
     problems.
 
-    Parameters
-    ----------
-
-    regressor : object, `default=LinearRegression()`
+    :param regressor: object, `default=LinearRegression()`
         Regressor object such as derived from ``RegressorMixin``. This
         regressor will automatically be cloned each time prior to fitting.
 
-    transformer : str or object of type :class:`BaseReciprocalTransformer
+    :param transformer: str or object of type :class:`BaseReciprocalTransformer
         <mlinsights.mlmodel.sklearn_transform_inv.BaseReciprocalTransformer>`
 
     .. runpython::
@@ -135,14 +132,11 @@ class TransformedTargetClassifier2(BaseEstimator, ClassifierMixin):
     Useful for applying permutation transformation in classification
     problems.
 
-    Parameters
-    ----------
-
-    classifier : object, default=LogisticRegression()
+    :param classifier: object, default=LogisticRegression()
         Classifier object such as derived from ``ClassifierMixin``. This
         classifier will automatically be cloned each time prior to fitting.
 
-    transformer : str or object of type :class:`BaseReciprocalTransformer
+    :param transformer: str or object of type :class:`BaseReciprocalTransformer
         <mlinsights.mlmodel.sklearn_transform_inv.BaseReciprocalTransformer>`
         Transforms the features.