removed unecessary option for cv

DuarteSJ · DuarteSJ · commit b8d97a5b197b · 2025-05-06T21:27:04.000+01:00
diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py
@@ -1,7 +1,7 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-from numbers import Integral, Real
+from numbers import Real
 
 import numpy as np
 
@@ -92,14 +92,24 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder):
         more weight on the global target mean.
         If `"auto"`, then `smooth` is set to an empirical Bayes estimate.
 
-    cv : int or cross-validation generator, default=5
-        Determines the number of folds in the :term:`cross fitting` strategy used in
-        :meth:`fit_transform`. For classification targets, `StratifiedKFold` is used
-        and for continuous targets, `KFold` is used.
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
 
-        If an integer is provided, it is the number of folds.
-        If a cross-validation generator is provided, it should be compatible with
-        scikit-learn's cross-validation interface.
+        - None, to use the default 5-fold cross validation,
+        - integer, to specify the number of folds in a `(Stratified)KFold`,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if the estimator is a classifier and ``y`` is
+        either binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other
+        cases, :class:`~sklearn.model_selection.KFold` is used. These splitters
+        are instantiated with `shuffle=False` so the splits will be the same
+        across calls.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
 
     shuffle : bool, default=True
         Whether to shuffle the data in :meth:`fit_transform` before splitting into
@@ -195,10 +205,7 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder):
         "categories": [StrOptions({"auto"}), list],
         "target_type": [StrOptions({"auto", "continuous", "binary", "multiclass"})],
         "smooth": [StrOptions({"auto"}), Interval(Real, 0, None, closed="left")],
-        "cv": [
-            Interval(Integral, 2, None, closed="left"),
-            "cv_object",
-        ],
+        "cv": ["cv_object"],
         "shuffle": ["boolean"],
         "random_state": ["random_state"],
     }
