KFold (original) (raw)
class sklearn.model_selection.KFold(n_splits=5, *, shuffle=False, random_state=None)[source]#
K-Fold 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.
For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn
Parameters:
n_splitsint, default=5
Number of folds. Must be at least 2.
Changed in version 0.22: n_splits default value changed from 3 to 5.
shufflebool, default=False
Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.
random_stateint, 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.
See also
Takes class 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
import numpy as np 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 i, (train_index, test_index) in enumerate(kf.split(X)): ... print(f"Fold {i}:") ... print(f" Train: index={train_index}") ... print(f" Test: index={test_index}") Fold 0: Train: index=[2 3] Test: index=[0 1] Fold 1: Train: index=[0 1] Test: index=[2 3]
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility.
Returns:
n_splitsint
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:
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
groupsobject
Always ignored, exists for compatibility.
Yields:
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split.