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scikit-learn 1.3.2
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# `sklearn.model_selection`.check_cv

sklearn.model_selection.check_cv(cv=5, y=None, *, classifier=False)

  Input checker utility for building a cross-validator.

  Parameters:
    cv int, cross-validation generator or an iterable, default=None

      Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds. - CV splitter, - An iterable that generates (train, test) splits as arrays of indices.

      For integer/None inputs, if classifier is True and `y` is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used.

      Refer User Guide for the various cross-validation strategies that can be used here.

      Changed in version 0.22: `cv` default value changed from 3-fold to 5-fold.

    y array-like, default=None

      The target variable for supervised learning problems.

    classifier bool, default=False

      Whether the task is a classification task, in which case stratified KFold will be used.

  Returns:
    checked_cv a cross-validator instance.

      The return value is a cross-validator which generates the train/test splits via the `split` method.