`sklearn.model_selection`.GroupKFold¶

class sklearn.model_selection.GroupKFold(n_splits=5)[source]¶

K-fold iterator variant with non-overlapping groups.

The same group will not appear in two different 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.

Parameters

n_splitsint, 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.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import GroupKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> groups = np.array([0, 0, 2, 2])
>>> group_kfold = GroupKFold(n_splits=2)
>>> group_kfold.get_n_splits(X, y, groups)
2
>>> print(group_kfold)
GroupKFold(n_splits=2)
>>> for train_index, test_index in group_kfold.split(X, y, groups):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...     print(X_train, X_test, y_train, y_test)
...
TRAIN: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]

Methods

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

__init__(self, n_splits=5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

get_n_splits(self, 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(self, X, y=None, groups=None)[source]¶

Generate indices to split data into training and test set.

Parameters

Xarray-like, 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, shape (n_samples,), optional: The target variable for supervised learning problems.
groupsarray-like, with 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.

sklearn.model_selection.GroupKFold¶

`sklearn.model_selection`.GroupKFold¶