estimator : estimator object implementing ‘fit’
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like
The target variable to try to predict in the case of supervised learning.
groups : array-like, with shape (n_samples,), optional
Labels to constrain permutation within groups, i.e.
y values are permuted among samples with the same group identifier. When not specified,
y values are permuted among all samples.
When a grouped cross-validator is used, the group labels are also passed on to the
split method of the cross-validator. The cross-validator uses them for grouping the samples while splitting the dataset into train/test set.
scoring : string, callable or None, optional, default: None
A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.
If None the estimator’s default scorer, if available, is used.
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 in a
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier 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 if None will change from 3-fold to 5-fold in v0.22.
n_permutations : integer, optional
Number of times to permute
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
None means 1 unless in a
-1 means using all processors. See Glossary for more details.
random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
verbose : integer, optional
The verbosity level.