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# `sklearn.model_selection`.KFold

class sklearn.model_selection.KFold(n_splits=5, *, shuffle=False, random_state=None)

  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.

  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_splits int, default=5

      Number of folds. Must be at least 2.

      Changed in version 0.22: `n_splits` default value changed from 3 to 5.

    shuffle bool, default=False

      Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.

    random_state int, 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

  `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()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_n_splits(X=None, y=None, groups=None)

    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.

    Returns:
      n_splits int

        Returns the number of splitting iterations in the cross-validator.

  split(X, y=None, groups=None)

    Generate indices to split data into training and test set.

    Parameters:
      X array-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.

      y array-like of shape (n_samples,), default=None

        The target variable for supervised learning problems.

      groups array-like of shape (n_samples,), default=None

        Group labels for the samples used while splitting the dataset into train/test set.

    Yields:
      train ndarray

        The training set indices for that split.

      test ndarray

        The testing set indices for that split.

## Examples using `sklearn.model_selection.KFold`

Feature agglomeration vs. univariate selection

Feature agglomeration vs. univariate selection

Comparing Random Forests and Histogram Gradient Boosting models

Comparing Random Forests and Histogram Gradient Boosting models

Gradient Boosting Out-of-Bag estimates

Gradient Boosting Out-of-Bag estimates

Nested versus non-nested cross-validation

Nested versus non-nested cross-validation

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

Cross-validation on diabetes Dataset Exercise

Cross-validation on diabetes Dataset Exercise