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_splitsint, default=5
Number of folds. Must be at least 2.
- n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
- random_stateint, RandomState instance or None, default=None
Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary.
See also
RepeatedKFold
Repeats K-Fold n times.
Notes
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.Examples
>>> import numpy as np >>> 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
- Xobject
Always ignored, exists for compatibility.
np.zeros(n_samples)
may be used as a placeholder.- yobject
Always ignored, exists for compatibility.
np.zeros(n_samples)
may be used as a placeholder.- groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set.
- Returns
- n_splitsint
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
- Xarray-like of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features.- yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
- groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set.
- Yields
- trainndarray
The training set indices for that split.
- testndarray
The testing set indices for that split.