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sklearn.model_selection.check_cv(cv=3, y=None, classifier=False)
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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.
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.
sklearn.model_selection.check_cv()
2017-01-15 04:26:45
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