Fix doc refs to StratifiedKFold and KFold (scikit-learn#6936)

Fix links to `StratifiedKFold` and `KFold` in docstrings.

Author: proinsias

sklearn/calibration.py

@@ -63,8 +63,9 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
-        :class:`StratifiedKFold` used. If ``y`` is neither binary nor
-        multiclass, :class:`KFold` is used.
+        :class:`sklearn.model_selection.StratifiedKFold` is used. If ``y`` 
+        is neither binary nor multiclass, :class:`sklearn.model_selection.KFold` 
+        is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

sklearn/cross_validation.py

@@ -1226,7 +1226,7 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -1411,7 +1411,7 @@ def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -1697,7 +1697,7 @@ def check_cv(cv, X=None, y=None, classifier=False):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if classifier is True and ``y`` is binary or
-        multiclass, :class:`StratifiedKFold` used. In all other cases,
+        multiclass, :class:`StratifiedKFold` is used. In all other cases,
         :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -1771,7 +1771,7 @@ def permutation_test_score(estimator, X, y, cv=None,
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various

sklearn/feature_selection/rfe.py

@@ -293,8 +293,9 @@ class RFECV(RFE, MetaEstimatorMixin):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
-        :class:`StratifiedKFold` used. If the estimator is a classifier
-        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
+        :class:`sklearn.model_selection.StratifiedKFold` is used. If the 
+        estimator is a classifier or if ``y`` is neither binary nor multiclass, 
+        :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

sklearn/grid_search.py

@@ -687,8 +687,9 @@ class GridSearchCV(BaseSearchCV):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
-        other cases, :class:`KFold` is used.
+        either binary or multiclass, 
+        :class:`sklearn.model_selection.StratifiedKFold` is used. In all
+        other cases, :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.
@@ -898,8 +899,9 @@ class RandomizedSearchCV(BaseSearchCV):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
-        other cases, :class:`KFold` is used.
+        either binary or multiclass, 
+        :class:`sklearn.model_selection.StratifiedKFold` is used. In all
+        other cases, :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

sklearn/learning_curve.py

@@ -75,8 +75,9 @@ def learning_curve(estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 5),
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
-        other cases, :class:`KFold` is used.
+        either binary or multiclass, 
+        :class:`sklearn.model_selection.StratifiedKFold` is used. In all
+        other cases, :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.
@@ -288,8 +289,9 @@ def validation_curve(estimator, X, y, param_name, param_range, cv=None,
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
-        other cases, :class:`KFold` is used.
+        either binary or multiclass, 
+        :class:`sklearn.model_selection.StratifiedKFold` is used. In all
+        other cases, :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

sklearn/linear_model/ridge.py

@@ -1131,7 +1131,8 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
-        :class:`StratifiedKFold` used, else, :class:`KFold` is used.
+        :class:`sklearn.model_selection.StratifiedKFold` is used, else, 
+        :class:`sklearn.model_selection.KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
         cross-validation strategies that can be used here.

sklearn/model_selection/_search.py

@@ -706,7 +706,7 @@ class GridSearchCV(BaseSearchCV):
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -962,7 +962,7 @@ class RandomizedSearchCV(BaseSearchCV):
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various

sklearn/model_selection/_split.py

@@ -1385,7 +1385,7 @@ def check_cv(cv=3, y=None, classifier=False):
           - An iterable yielding train/test splits.
 
         For integer/None inputs, if classifier is True and ``y`` is either
-        binary or multiclass, :class:`StratifiedKFold` used. In all other
+        binary or multiclass, :class:`StratifiedKFold` is used. In all other
         cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various

sklearn/model_selection/_validation.py

@@ -98,7 +98,7 @@ def cross_val_score(estimator, X, y=None, labels=None, scoring=None, cv=None,
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -346,7 +346,7 @@ def cross_val_predict(estimator, X, y=None, labels=None, cv=None, n_jobs=1,
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -562,7 +562,7 @@ def permutation_test_score(estimator, X, y, labels=None, cv=None,
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -702,7 +702,7 @@ def learning_curve(estimator, X, y, labels=None,
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various
@@ -915,7 +915,7 @@ def validation_curve(estimator, X, y, param_name, param_range, labels=None,
           - An iterable yielding train, test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, :class:`StratifiedKFold` used. In all
+        either binary or multiclass, :class:`StratifiedKFold` is used. In all
         other cases, :class:`KFold` is used.
 
         Refer :ref:`User Guide <cross_validation>` for the various