Parameters: |
-
estimator : object type that implements the “fit” and “predict” methods -
An object of that type which is cloned for each validation. -
X : array-like, shape (n_samples, n_features) -
Training vector, where n_samples is the number of samples and n_features is the number of features. -
y : array-like, shape (n_samples) or (n_samples, n_features), optional -
Target relative to X for classification or regression; None for unsupervised learning. -
param_name : string -
Name of the parameter that will be varied. -
param_range : array-like, shape (n_values,) -
The values of the parameter that will be evaluated. -
groups : array-like, with shape (n_samples,), optional -
Group labels for the samples used while splitting the dataset into train/test set. -
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. -
scoring : string, callable or None, optional, default: None -
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y) . -
n_jobs : int or None, optional (default=None) -
Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. -
pre_dispatch : integer or string, optional -
Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like ‘2*n_jobs’. -
verbose : integer, optional -
Controls the verbosity: the higher, the more messages. -
error_score : ‘raise’ | ‘raise-deprecating’ or numeric -
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If set to ‘raise-deprecating’, a FutureWarning is printed before the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Default is ‘raise-deprecating’ but from version 0.22 it will change to np.nan. |