class sklearn.model_selection.KFold(n_splits=’warn’, shuffle=False, random_state=None)
Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
Each fold is then used once as a validation while the k - 1 remaining folds form the training set.
Read more in the User Guide.
n_samples % n_splits folds have size
n_samples // n_splits + 1, other folds have size
n_samples // n_splits, where
n_samples is the number of samples.
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.
>>> from sklearn.model_selection import KFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4]) >>> kf = KFold(n_splits=2) >>> kf.get_n_splits(X) 2 >>> print(kf) KFold(n_splits=2, random_state=None, shuffle=False) >>> for train_index, test_index in kf.split(X): ... 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: [2 3] TEST: [0 1] TRAIN: [0 1] TEST: [2 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=None, groups=None)
Generate indices to split data into training and test set.
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