sklearn.model_selection.RepeatedKFold — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.model_selection. RepeatedKFold(n_splits=5, n_repeats=10, random_state=None)[source]¶
Repeated K-Fold cross validator.
Repeats K-Fold n times with different randomization in each repetition.
Read more in the User Guide.
| Parameters: | n_splits : int, default=5 Number of folds. Must be at least 2. n_repeats : int, default=10 Number of times cross-validator needs to be repeated. random_state : int, RandomState instance or None, optional, default=None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. |
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Notes
Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_stateto an integer.
Examples
from sklearn.model_selection import RepeatedKFold X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) y = np.array([0, 0, 1, 1]) rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124) for train_index, test_index in rkf.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: [0 1] TEST: [2 3] TRAIN: [2 3] TEST: [0 1] TRAIN: [1 2] TEST: [0 3] TRAIN: [0 3] TEST: [1 2]
Methods
| get_n_splits([X, y, groups]) | Returns the number of splitting iterations in the cross-validator |
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| split(X[, y, groups]) | Generates indices to split data into training and test set. |
__init__(n_splits=5, n_repeats=10, random_state=None)[source]¶
get_n_splits(X=None, y=None, groups=None)[source]¶
Returns the number of splitting iterations in the cross-validator
| Parameters: | X : object Always ignored, exists for compatibility.np.zeros(n_samples) may be used as a placeholder. y : object Always ignored, exists for compatibility.np.zeros(n_samples) may be used as a placeholder. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. |
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| Returns: | n_splits : int Returns the number of splitting iterations in the cross-validator. |
split(X, y=None, groups=None)[source]¶
Generates indices to split data into training and test set.
| Parameters: | X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. y : array-like, of length n_samples The target variable for supervised learning problems. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. |
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| Yields: | train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split. |