Parameters: |
-
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
(Stratified)KFold , - 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 y . -
n_jobs : int or None, optional (default=None) -
The number of CPUs to use to do the computation. None means 1 unless in a joblib.parallel_backend context. -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 np.random . -
verbose : integer, optional -
The verbosity level. |