DOC Add note on overlapping test sets in GroupShuffleSplit (#29676)

lucyleeow · web-flow · commit 635d83f090d1 · 2024-08-23T12:15:22.000+02:00
diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
@@ -1932,8 +1932,9 @@ class ShuffleSplit(_UnsupportedGroupCVMixin, BaseShuffleSplit):
     Yields indices to split data into training and test sets.
 
     Note: contrary to other cross-validation strategies, random splits
-    do not guarantee that all folds will be different, although this is
-    still very likely for sizeable datasets.
+    do not guarantee that test sets across all folds will be mutually exclusive,
+    and might include overlapping samples. However, this is still very likely for
+    sizeable datasets.
 
     Read more in the :ref:`User Guide <ShuffleSplit>`.
 
@@ -2049,6 +2050,11 @@ class GroupShuffleSplit(GroupsConsumerMixin, BaseShuffleSplit):
     ``LeavePGroupsOut(p=10)`` would be
     ``GroupShuffleSplit(test_size=10, n_splits=100)``.
 
+    Contrary to other cross-validation strategies, the random splits
+    do not guarantee that test sets across all folds will be mutually exclusive,
+    and might include overlapping samples. However, this is still very likely for
+    sizeable datasets.
+
     Note: The parameters ``test_size`` and ``train_size`` refer to groups, and
     not to samples as in :class:`ShuffleSplit`.
 
@@ -2176,13 +2182,14 @@ class StratifiedShuffleSplit(BaseShuffleSplit):
 
     Provides train/test indices to split data in train/test sets.
 
-    This cross-validation object is a merge of StratifiedKFold and
-    ShuffleSplit, which returns stratified randomized folds. The folds
+    This cross-validation object is a merge of :class:`StratifiedKFold` and
+    :class:`ShuffleSplit`, which returns stratified randomized folds. The folds
     are made by preserving the percentage of samples for each class.
 
-    Note: like the ShuffleSplit strategy, stratified random splits
-    do not guarantee that all folds will be different, although this is
-    still very likely for sizeable datasets.
+    Note: like the :class:`ShuffleSplit` strategy, stratified random splits
+    do not guarantee that test sets across all folds will be mutually exclusive,
+    and might include overlapping samples. However, this is still very likely for
+    sizeable datasets.
 
     Read more in the :ref:`User Guide <stratified_shuffle_split>`.
 
