sklearn.model_selection
.GroupKFold¶

class
sklearn.model_selection.
GroupKFold
(n_splits=3)[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_splits : int, default=3
Number of folds. Must be at least 2.
See also
LeaveOneGroupOut
 For splitting the data according to explicit domainspecific stratification of the dataset.
Examples
>>> 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
([X, y, groups])Returns the number of splitting iterations in the crossvalidator 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 crossvalidator
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 crossvalidator.

split
(X, y=None, groups=None)[source]¶ Generate indices to split data into training and test set.
Parameters: X : arraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape (n_samples,)
The target variable for supervised learning problems.
groups : arraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.