`sklearn.model_selection`.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 distinct groups is approximately the same in each 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]

Methods

`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_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,): 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.GroupKFold`¶

Visualizing cross-validation behavior in scikit-learn

sklearn.model_selection.GroupKFold¶

Examples using sklearn.model_selection.GroupKFold¶

`sklearn.model_selection`.GroupKFold¶

Examples using `sklearn.model_selection.GroupKFold`¶