sklearn.model_selection.GridSearchCV  
class sklearn.model_selection.GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False) [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 “score_samples”, “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 cross-validated grid-search over a parameter grid. Read more in the User Guide.  Parameters 
 
estimatorestimator object. 

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.  
param_griddict or list of dictionaries 

Dictionary with parameters names (str) 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.  
scoringstr, callable, list, tuple or dict, default=None 

Strategy to evaluate the performance of the cross-validated model on the test set. If scoring represents a single score, one can use:  a single string (see The scoring parameter: defining model evaluation rules); a callable (see Defining your scoring strategy from metric functions) that returns a single value.  If scoring reprents multiple scores, one can use:  a list or tuple of unique strings; a callable returning a dictionary where the keys are the metric names and the values are the metric scores; a dictionary with metric names as keys and callables a values.  See Specifying multiple metrics for evaluation for an example.  
n_jobsint, 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.  Changed in version v0.20: n_jobs default changed from 1 to None   
pre_dispatchint, or str, default=n_jobs 

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 str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’   
cvint, cross-validation generator or an iterable, default=None 

Determines the cross-validation splitting strategy. Possible inputs for cv are:  None, to use the default 5-fold 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 cross-validation strategies that can be used here.  Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.   
refitbool, str, or callable, default=True 

Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a str denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, refit can be set to a function which returns the selected best_index_ given cv_results_. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this GridSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit 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.  Changed in version 0.20: Support for callable added.   
verboseint 

Controls the verbosity: the higher, the more messages.  >1 : the computation time for each fold and parameter candidate is displayed; >2 : the score is also displayed; >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation.   
error_score‘raise’ or numeric, default=np.nan 

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.  
return_train_scorebool, default=False 

If False, the cv_results_ attribute will not include training scores. 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.  New in version 0.19.   Changed in version 0.21: Default value was changed from True to False     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 and std_score_time are all in seconds. For multi-metric 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 

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 cross-validated score of the best_estimator For multi-metric evaluation, this is present only if refit is specified. This attribute is not available if refit is a function.  
best_params_dict 

Parameter setting that gave the best results on the hold out data. For multi-metric 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 multi-metric 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 multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.  
n_splits_int 

The number of cross-validation 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.  New in version 0.20.   
multimetric_bool 

Whether or not the scorers compute several metrics.      See also  
ParameterGrid


Generates all the combinations of a hyperparameter grid.  
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()
>>> clf = GridSearchCV(svc, parameters)
>>> clf.fit(iris.data, iris.target)
GridSearchCV(estimator=SVC(),
             param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
>>> sorted(clf.cv_results_.keys())
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split2_test_score', ...
 'std_fit_time', 'std_score_time', 'std_test_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.  
score_samples(X) Call score_samples on the estimator with the best found parameters.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Call transform on the estimator with the best found parameters.    
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 
 
Xindexable, 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 
 
Xarray-like of shape (n_samples, n_features) 

Training vector, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples, n_output) or (n_samples,), default=None 

Target relative to X for classification or regression; None for unsupervised learning.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).  
**fit_paramsdict of str -> object 

Parameters passed to the fit method of the estimator     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

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 and refit=True.  Parameters 
 
Xtindexable, 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 supports predict.  Parameters 
 
Xindexable, 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 supports predict_log_proba.  Parameters 
 
Xindexable, 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 supports predict_proba.  Parameters 
 
Xindexable, 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 the best_estimator_.score method otherwise.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Input data, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples, n_output) or (n_samples,), default=None 

Target relative to X for classification or regression; None for unsupervised learning.    Returns 
 
scorefloat 
   
  
score_samples(X) [source]
 
Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New in version 0.24.   Parameters 
 
Xiterable 

Data to predict on. Must fulfill input requirements of the underlying estimator.    Returns 
 
y_scorendarray of shape (n_samples,) 
   
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
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 
 
Xindexable, length n_samples 

Must fulfill the input assumptions of the underlying estimator.     
 
 Examples using sklearn.model_selection.GridSearchCV
 
  Release Highlights for scikit-learn 0.24  

  Feature agglomeration vs. univariate selection  

  Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood  

  Model selection with Probabilistic PCA and Factor Analysis (FA)  

  Faces recognition example using eigenfaces and SVMs  

  Comparison of kernel ridge and Gaussian process regression  

  Comparison of kernel ridge regression and SVR  

  Parameter estimation using grid search with cross-validation  

  Comparing randomized search and grid search for hyperparameter estimation  

  Nested versus non-nested cross-validation  

  Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV  

  Sample pipeline for text feature extraction and evaluation  

  Balance model complexity and cross-validated score  

  Comparison between grid search and successive halving  

  Statistical comparison of models using grid search  

  Kernel Density Estimation  

  Caching nearest neighbors  

  Concatenating multiple feature extraction methods  

  Pipelining: chaining a PCA and a logistic regression  

  Selecting dimensionality reduction with Pipeline and GridSearchCV  

  Column Transformer with Mixed Types  

  Feature discretization  

  Scaling the regularization parameter for SVCs  

  RBF SVM parameters  

  Cross-validation on diabetes Dataset Exercise