sklearn.model_selection
.GridSearchCV¶

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:  estimator : estimator object.
This is assumed to implement the scikitlearn estimator interface. Either estimator needs to provide a
score
function, orscoring
must be passed. param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
 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.
 fit_params : dict, optional
Parameters to pass to the fit method.
Deprecated since version 0.19:
fit_params
as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to thefit
method instead. n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. 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 fastrunning jobs, to avoid delays due to ondemand 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’
 iid : boolean, default=’warn’
If True, return the average score across folds, weighted by the number of samples in each test set. In this case, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds. If False, return the average score across folds. Default is True, but will change to False in version 0.21, to correspond to the standard definition of crossvalidation.
Changed in version 0.20: Parameter
iid
will change from True to False by default in version 0.22, and will be removed in 0.24. cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold cross validation,
 integer, to specify the number of folds in a (Stratified)KFold,
 CV splitter,
 An iterable yielding (train, test) splits as arrays of indices.
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 crossvalidation strategies that can be used here.
Changed in version 0.20:
cv
default value if None will change from 3fold to 5fold in v0.22. refit : boolean, or string, default=True
Refit an estimator using the best found parameters on the whole dataset.
For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end.
The refitted estimator is made available at the
best_estimator_
attribute and permits usingpredict
directly on thisGridSearchCV
instance.Also for multiple metric evaluation, the attributes
best_index_
,best_score_
andbest_params_
will only be available ifrefit
is set and all of them will be determined w.r.t this specific scorer.See
scoring
parameter to know more about multiple metric evaluation. verbose : integer
Controls the verbosity: the higher, the more messages.
 error_score : ‘raise’ or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, 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’ but from version 0.22 it will change to np.nan.
 return_train_score : boolean, optional
If
False
, thecv_results_
attribute will not include training scores.Current default is
'warn'
, which behaves asTrue
in addition to raising a warning when a training score is looked up. That default will be changed toFalse
in 0.21. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting tradeoff. 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.
Attributes:  cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be imported into a pandas
DataFrame
.For instance the below given table
param_kernel param_gamma param_degree split0_test_score … rank_t… ‘poly’ – 2 0.80 … 2 ‘poly’ – 3 0.70 … 4 ‘rbf’ 0.1 – 0.80 … 3 ‘rbf’ 0.2 – 0.93 … 1 will be represented by a
cv_results_
dict of:{ 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], mask = [False False False False]...) 'param_gamma': masked_array(data = [  0.1 0.2], mask = [ True True False False]...), 'param_degree': masked_array(data = [2.0 3.0  ], mask = [False False True True]...), 'split0_test_score' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.70, 0.78], 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'split0_train_score' : [0.80, 0.92, 0.70, 0.93], 'split1_train_score' : [0.82, 0.55, 0.70, 0.87], 'mean_train_score' : [0.81, 0.74, 0.70, 0.90], 'std_train_score' : [0.01, 0.19, 0.00, 0.03], 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49], 'std_fit_time' : [0.01, 0.02, 0.01, 0.01], 'mean_score_time' : [0.01, 0.06, 0.04, 0.04], 'std_score_time' : [0.00, 0.00, 0.00, 0.01], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], }
NOTE
The key
'params'
is used to store a list of parameter settings dicts for all the parameter candidates.The
mean_fit_time
,std_fit_time
,mean_score_time
andstd_score_time
are all in seconds.For multimetric evaluation, the scores for all the scorers are available in the
cv_results_
dict at the keys ending with that scorer’s name ('_<scorer_name>'
) instead of'_score'
shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.) best_estimator_ : estimator or dict
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if
refit=False
.See
refit
parameter for more information on allowed values. best_score_ : float
Mean crossvalidated score of the best_estimator
For multimetric evaluation, this is present only if
refit
is specified. best_params_ : dict
Parameter setting that gave the best results on the hold out data.
For multimetric evaluation, this is present only if
refit
is specified. best_index_ : int
The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting.The dict at
search.cv_results_['params'][search.best_index_]
gives the parameter setting for the best model, that gives the highest mean score (search.best_score_
).For multimetric evaluation, this is present only if
refit
is specified. scorer_ : function or a dict
Scorer function used on the held out data to choose the best parameters for the model.
For multimetric evaluation, this attribute holds the validated
scoring
dict which maps the scorer key to the scorer callable. n_splits_ : int
The number of crossvalidation splits (folds/iterations).
 refit_time_ : float
Seconds used for refitting the best model on the whole dataset.
This is present only if
refit
is not False.
See also
ParameterGrid
 generates all the combinations of a hyperparameter grid.
sklearn.model_selection.train_test_split
 utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.
sklearn.metrics.make_scorer
 Make a scorer from a performance metric or loss function.
Notes
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.
Examples
>>> 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'...]
Methods
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 supportsdecision_function
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

fit
(X, y=None, groups=None, **fit_params)[source]¶ Run fit with all sets of parameters.
Parameters:  X : arraylike, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
 groups : arraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
 **fit_params : dict of string > object
Parameters passed to the
fit
method of the estimator

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

inverse_transform
(Xt)[source]¶ Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements
inverse_transform
andrefit=True
.Parameters:  Xt : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

predict
(X)[source]¶ Call predict on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

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 supportspredict_log_proba
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

predict_proba
(X)[source]¶ Call predict_proba on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict_proba
.Parameters:  X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.

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 thebest_estimator_.score
method otherwise.Parameters:  X : arraylike, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
Returns:  score : float

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:  self