Method split

sklearn/model_selection/_split.py:1420–1444

Generate indices to split data into training and test set. Parameters ---------- X : array-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. y : array-l

(self, X, y=None, groups=None)

        return len(np.unique(groups))

    def split(self, X, y=None, groups=None):
        """Generate indices to split data into training and test set.

        Parameters
        ----------
        X : array-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.

        y : array-like of shape (n_samples,), default=None
            The target variable for supervised learning problems.

        groups : array-like of shape (n_samples,)
            Group labels for the samples used while splitting the dataset into
            train/test set.

        Yields
        ------
        train : ndarray
            The training set indices for that split.

        test : ndarray
            The testing set indices for that split.
        """
        return super().split(X, y, groups)


class LeavePGroupsOut(GroupsConsumerMixin, BaseCrossValidator):