sklearn.model_selection.KFold — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.model_selection. KFold(n_splits='warn', shuffle=False, random_state=None)[source]¶
K-Folds cross-validator
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.
| Parameters: | n_splits : int, default=3 Number of folds. Must be at least 2. Changed in version 0.20: n_splits default value will change from 3 to 5 in v0.22. shuffle : boolean, optional Whether to shuffle the data before splitting into batches. 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. Used when shuffle == True. |
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See also
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.
Notes
The first n_samples % n_splits folds have sizen_samples // n_splits + 1, other folds have sizen_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_stateto an integer.
Examples
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]
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]) | Generate indices to split data into training and test set. |
__init__(n_splits='warn', shuffle=False, 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. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility. |
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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]¶
Generate 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, shape (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. |