sklearn.model_selection.check_cv

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

Input checker utility for building a cross-validator

Parameters:

cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 3-fold cross-validation, integer, to specify the number of folds. An object to be used as a cross-validation generator. An iterable yielding train/test splits. 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 0.20: cv default value will change from 3-fold to 5-fold in v0.22. y : array-like, optional The target variable for supervised learning problems. classifier : boolean, optional, 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.