- class mars.learn.model_selection.KFold(n_splits=5, *, 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.
n_splits (int, default=5) –
Number of folds. Must be at least 2.
Changed in version 0.22:
n_splitsdefault value changed from 3 to 5.
shuffle (bool, default=False) – Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.
random_state (int, RandomState instance or None, default=None) – When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary.
>>> import mars.tensor as mt >>> from mars.learn.model_selection import KFold >>> X = mt.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = mt.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]
n_samples % n_splitsfolds have size
n_samples // n_splits + 1, other folds have size
n_samples // n_splits, where
n_samplesis 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.
Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
K-fold iterator variant with non-overlapping groups.
Repeats K-Fold n times.
- __init__(n_splits=5, *, shuffle=False, random_state=None)#
__init__([n_splits, shuffle, random_state])
get_n_splits([X, y, groups])
Returns the number of splitting iterations in the cross-validator
split(X[, y, groups])
Generate indices to split data into training and test set.