class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, *, n_iter=10, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score=nan, return_train_score=False) [source]
 
Randomized search on hyper parameters. RandomizedSearchCV 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 search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide.  New in version 0.14.   Parameters 
 
estimatorestimator object. 

A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.  
param_distributionsdict or list of dicts 

Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.  
n_iterint, default=10 

Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.  
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. If None, the estimator’s score method is used.  
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=None 

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 the 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 RandomizedSearchCV 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.  
random_stateint, RandomState instance or None, default=None 

Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.  
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 split0_test_score … rank_test_score   
‘rbf’ 0.1 0.80 … 1  
‘rbf’ 0.2 0.84 … 3  
‘rbf’ 0.3 0.70 … 2   will be represented by a cv_results_ dict of: {
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
                              mask = False),
'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score'  : [0.80, 0.84, 0.70],
'split1_test_score'  : [0.82, 0.50, 0.70],
'mean_test_score'    : [0.81, 0.67, 0.70],
'std_test_score'     : [0.01, 0.24, 0.00],
'rank_test_score'    : [1, 3, 2],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score'   : [0.81, 0.74, 0.70],
'std_train_score'    : [0.01, 0.19, 0.00],
'mean_fit_time'      : [0.73, 0.63, 0.43],
'std_fit_time'       : [0.01, 0.02, 0.01],
'mean_score_time'    : [0.01, 0.06, 0.04],
'std_score_time'     : [0.00, 0.00, 0.00],
'params'             : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
 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. For multi-metric evaluation, this attribute is present only if refit is specified. 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 not available if refit is False. See refit parameter for more information. 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 not available if refit is False. See refit parameter for more information.  
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 not available if refit is False. See refit parameter for more information.  
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  
GridSearchCV


Does exhaustive search over a grid of parameters.  
ParameterSampler


A generator over parameter settings, constructed from param_distributions.    Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. If n_jobs was set to a value higher than one, the data is copied for each parameter setting(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.datasets import load_iris
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import RandomizedSearchCV
>>> from scipy.stats import uniform
>>> iris = load_iris()
>>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,
...                               random_state=0)
>>> distributions = dict(C=uniform(loc=0, scale=4),
...                      penalty=['l2', 'l1'])
>>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)
>>> search = clf.fit(iris.data, iris.target)
>>> search.best_params_
{'C': 2..., 'penalty': 'l1'}
 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.