sklearn.cross_validation.check_cv

sklearn.cross_validation.check_cv(cv, X=None, y=None, classifier=False) [source]

Input checker utility for building a CV in a user friendly way.

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.check_cv instead.

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 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. X : array-like The data the cross-val object will be applied on. y : array-like The target variable for a supervised learning problem. classifier : boolean optional Whether the task is a classification task, in which case stratified KFold will be used.

Returns:

checked_cv : a cross-validation generator instance. The return value is guaranteed to be a cv generator instance, whatever the input type.