sklearn.model_selection
.RepeatedStratifiedKFold¶
-
class
sklearn.model_selection.
RepeatedStratifiedKFold
(n_splits=5, n_repeats=10, random_state=None)[source]¶ Repeated Stratified K-Fold cross validator.
Repeats Stratified 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 : None, int or RandomState, default=None
Random state to be used to generate random state for each repetition.
See also
RepeatedKFold
- Repeats K-Fold n times.
Examples
>>> from sklearn.model_selection import RepeatedStratifiedKFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2, ... random_state=36851234) >>> for train_index, test_index in rskf.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 2] TEST: [0 3] TRAIN: [0 3] TEST: [1 2] TRAIN: [1 3] TEST: [0 2] TRAIN: [0 2] TEST: [1 3]
Methods
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the cross-validator split
(X[, y, groups])Generates indices to split data into training and test set. -
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.
Returns: n_splits : int
Returns the number of splitting iterations in the cross-validator.
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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.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.