Parameters: |
-
estimator : estimator object implementing ‘fit’ -
The object to use to fit the data. -
X : array-like -
The data to fit. Can be for example a list, or an array. -
y : array-like, optional, default: None -
The target variable to try to predict in the case of supervised learning. -
groups : array-like, with shape (n_samples,), optional -
Group labels for the samples used while splitting the dataset into train/test set. -
scoring : string, callable, list/tuple, dict or None, 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. For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values. NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each. See Specifying multiple metrics for evaluation for an example. 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_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. -
verbose : integer, optional -
The verbosity level. -
fit_params : dict, optional -
Parameters to pass to the fit method of the estimator. -
pre_dispatch : int, or string, optional -
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
- An int, giving the exact number of total jobs that are spawned
- A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
-
return_train_score : boolean, optional -
Whether to include train scores. Current default is 'warn' , which behaves as True in addition to raising a warning when a training score is looked up. That default will be changed to False in 0.21. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. -
return_estimator : boolean, default False -
Whether to return the estimators fitted on each split. -
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. |