GroupKfold#

class sklearn.model_selection.GroupKfold(n_splits=5)[source]#

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 samples is approximately the same in each test fold.

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]

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.

set_split_request(*, groups: bool | None | str = '$UNCHANGED$') → GroupKfold[source]#

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.

Added 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:

groupsstr, True, false, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for groups parameter in split.

Returns:

selfobject: The updated object.

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,): 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.

Gallery examples#

Release Highlights for scikit-learn 1.4

Visualizing cross-validation behavior in scikit-learn