class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=None, iid=’warn’, refit=True, cv=’warn’, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raisedeprecating’, return_train_score=’warn’)
[source]
Exhaustive search over specified parameter values for an estimator.
Important members are fit, predict.
GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
The parameters of the estimator used to apply these methods are optimized by crossvalidated gridsearch over a parameter grid.
Read more in the User Guide.
Parameters: 
 

Attributes: 

See also
ParameterGrid
sklearn.model_selection.train_test_split
sklearn.metrics.make_scorer
The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.
If n_jobs
was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs
times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch
. Then, the memory is copied only pre_dispatch
many times. A reasonable value for pre_dispatch
is 2 * n_jobs
.
>>> from sklearn import svm, datasets >>> from sklearn.model_selection import GridSearchCV >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svc = svm.SVC(gamma="scale") >>> clf = GridSearchCV(svc, parameters, cv=5) >>> clf.fit(iris.data, iris.target) ... GridSearchCV(cv=5, error_score=..., estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=..., decision_function_shape='ovr', degree=..., gamma=..., kernel='rbf', max_iter=1, probability=False, random_state=None, shrinking=True, tol=..., verbose=False), fit_params=None, iid=..., n_jobs=None, param_grid=..., pre_dispatch=..., refit=..., return_train_score=..., scoring=..., verbose=...) >>> sorted(clf.cv_results_.keys()) ... ['mean_fit_time', 'mean_score_time', 'mean_test_score',... 'mean_train_score', 'param_C', 'param_kernel', 'params',... 'rank_test_score', 'split0_test_score',... 'split0_train_score', 'split1_test_score', 'split1_train_score',... 'split2_test_score', 'split2_train_score',... 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]
decision_function (X)  Call decision_function on the estimator with the best found parameters. 
fit (X[, y, groups])  Run fit with all sets of parameters. 
get_params ([deep])  Get parameters for this estimator. 
inverse_transform (Xt)  Call inverse_transform on the estimator with the best found params. 
predict (X)  Call predict on the estimator with the best found parameters. 
predict_log_proba (X)  Call predict_log_proba on the estimator with the best found parameters. 
predict_proba (X)  Call predict_proba on the estimator with the best found parameters. 
score (X[, y])  Returns the score on the given data, if the estimator has been refit. 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Call transform on the estimator with the best found parameters. 
__init__(estimator, param_grid, scoring=None, fit_params=None, n_jobs=None, iid=’warn’, refit=True, cv=’warn’, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raisedeprecating’, return_train_score=’warn’)
[source]
decision_function(X)
[source]
Call decision_function on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports decision_function
.
Parameters: 


fit(X, y=None, groups=None, **fit_params)
[source]
Run fit with all sets of parameters.
Parameters: 


get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

inverse_transform(Xt)
[source]
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements inverse_transform
and refit=True
.
Parameters: 


predict(X)
[source]
Call predict on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports predict
.
Parameters: 


predict_log_proba(X)
[source]
Call predict_log_proba on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports predict_log_proba
.
Parameters: 


predict_proba(X)
[source]
Call predict_proba on the estimator with the best found parameters.
Only available if refit=True
and the underlying estimator supports predict_proba
.
Parameters: 


score(X, y=None)
[source]
Returns the score on the given data, if the estimator has been refit.
This uses the score defined by scoring
where provided, and the best_estimator_.score
method otherwise.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


transform(X)
[source]
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform
and refit=True
.
Parameters: 


sklearn.model_selection.GridSearchCV
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html