sklearn.model_selection.check_cv(cv=5, y=None, classifier=False)[source]

Input checker utility for building a cross-validator

cvint, cross-validation generator or an iterable, optional

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 yielding (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.

yarray-like, optional

The target variable for supervised learning problems.

classifierboolean, optional, default False

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

checked_cva cross-validator instance.

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