Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.model_selection`.GroupKFold

class sklearn.model_selection.GroupKFold(n_splits=5)

  K-fold iterator variant with non-overlapping groups.

  Each group will appear exactly once in the test set across all folds (the number of distinct groups has to be at least equal to the number of folds).

  The folds are approximately balanced in the sense that the number of distinct groups is approximately the same in each fold.

  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.

  See also

  `LeaveOneGroupOut`

    For splitting the data according to explicit domain-specific stratification of the dataset.

  `StratifiedKFold`

    Takes class information into account to avoid building folds with imbalanced class proportions (for binary or multiclass classification tasks).

  Notes

  Groups appear in an arbitrary order throughout the folds.

  Examples

      >>> import numpy as np
      >>> from sklearn.model_selection import GroupKFold
      >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
      >>> y = np.array([1, 2, 3, 4, 5, 6])
      >>> groups = np.array([0, 0, 2, 2, 3, 3])
      >>> group_kfold = GroupKFold(n_splits=2)
      >>> group_kfold.get_n_splits(X, y, groups)
      2
      >>> print(group_kfold)
      GroupKFold(n_splits=2)
      >>> for i, (train_index, test_index) in enumerate(group_kfold.split(X, y, groups)):
      ...     print(f"Fold {i}:")
      ...     print(f"  Train: index={train_index}, group={groups[train_index]}")
      ...     print(f"  Test:  index={test_index}, group={groups[test_index]}")
      Fold 0:
        Train: index=[2 3], group=[2 2]
        Test:  index=[0 1 4 5], group=[0 0 3 3]
      Fold 1:
        Train: index=[0 1 4 5], group=[0 0 3 3]
        Test:  index=[2 3], group=[2 2]

  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
  `set_split_request`(*[, groups]) | Request metadata passed to the `split` method.
  `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.

  set_split_request(*, groups: bool | None | str = '$UNCHANGED$') → GroupKFold

    Request metadata passed to the `split` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `split` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `split`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      groups str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `groups` parameter in `split`.

    Returns:
      self object

        The updated object.

  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,)

        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.GroupKFold`

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn