`sklearn.model_selection`.KFold¶

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.

See also

StratifiedKFold: Takes class information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
GroupKFold: K-fold iterator variant with non-overlapping groups.
RepeatedKFold: Repeats K-Fold n times.

Notes

The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_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_state to 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]

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_n_splits`([X, y, groups])	Returns the number of splitting iterations in the cross-validator.
`split`(X[, y, groups])	Generate indices to split data into training and test set.

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,), default=None: 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.

Examples using `sklearn.model_selection.KFold`¶

Feature agglomeration vs. univariate selection

Comparing Random Forests and Histogram Gradient Boosting models

Gradient Boosting Out-of-Bag estimates

Nested versus non-nested cross-validation

Visualizing cross-validation behavior in scikit-learn

Cross-validation on diabetes Dataset Exercise

sklearn.model_selection.KFold¶

Examples using sklearn.model_selection.KFold¶

`sklearn.model_selection`.KFold¶

Examples using `sklearn.model_selection.KFold`¶