- sklearn.model_selection.check_cv(cv=5, y=None, *, classifier=False)¶
Input checker utility for building a cross-validator
- cvint, 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 yielding (train, test) splits as arrays of indices.
Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.22:
cvdefault value changed from 3-fold to 5-fold.
- yarray-like, default=None
The target variable for supervised learning problems.
- classifierbool, 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