class sklearn.model_selection.StratifiedKFold(n_splits=’warn’, shuffle=False, random_state=None)
Stratified K-Folds cross-validator
Provides train/test indices to split data in train/test sets.
This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.
Read more in the User Guide.
Train and test sizes may be different in each fold, with a difference of at most
>>> from sklearn.model_selection import StratifiedKFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> skf = StratifiedKFold(n_splits=2) >>> skf.get_n_splits(X, y) 2 >>> print(skf) StratifiedKFold(n_splits=2, random_state=None, shuffle=False) >>> for train_index, test_index in skf.split(X, y): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] TRAIN: [1 3] TEST: [0 2] TRAIN: [0 2] TEST: [1 3]
||Returns the number of splitting iterations in the cross-validator|
||Generate indices to split data into training and test set.|
get_n_splits(X=None, y=None, groups=None)
Returns the number of splitting iterations in the cross-validator
split(X, y, groups=None)
Generate indices to split data into training and test set.
Randomized CV splitters may return different results for each call of split. You can make the results identical by setting
random_state to an integer.
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