sklearn.model_selection
.GroupKFold¶

class
sklearn.model_selection.
GroupKFold
(n_splits=5)[source]¶ Kfold iterator variant with nonoverlapping 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 domainspecific 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 crossvalidator
split
(self, X[, y, groups])Generate indices to split data into training and test set.

get_n_splits
(self, X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
 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 crossvalidator.

split
(self, X, y=None, groups=None)[source]¶ Generate indices to split data into training and test set.
 Parameters
 Xarraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 yarraylike, shape (n_samples,), optional
The target variable for supervised learning problems.
 groupsarraylike, 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.