sklearn.model_selection
.RandomizedSearchCV¶

class
sklearn.model_selection.
RandomizedSearchCV
(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True)[source]¶ Randomized search on hyper parameters.
RandomizedSearchCV 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 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.
Parameters: estimator : estimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikitlearn estimator interface. Either estimator needs to provide a
score
function, orscoring
must be passed.param_distributions : dict
Dictionary with parameters names (string) 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.n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. IfNone
, thescore
method of the estimator is used.n_jobs : int, default=1
Number of jobs to run in parallel.
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=True
If True, 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.
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,
 An object to be used as a crossvalidation 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 crossvalidation strategies that can be used here.
refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int, RandomState instance or None, optional, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
error_score : ‘raise’ (default) 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.
return_train_score : boolean, default=True
If
'False'
, thecv_results_
attribute will not include training scores.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 0.8 ... 2 ‘rbf’ 0.2 0.9 ... 1 ‘rbf’ 0.3 0.7 ... 1 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' : [0.8, 0.9, 0.7], 'split1_test_score' : [0.82, 0.5, 0.7], 'mean_test_score' : [0.81, 0.7, 0.7], 'std_test_score' : [0.02, 0.2, 0.], 'rank_test_score' : [3, 1, 1], 'split0_train_score' : [0.8, 0.9, 0.7], 'split1_train_score' : [0.82, 0.5, 0.7], 'mean_train_score' : [0.81, 0.7, 0.7], 'std_train_score' : [0.03, 0.03, 0.04], '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' : [0.007, 0.06, 0.04, 0.04], 'std_score_time' : [0.001, 0.002, 0.003, 0.005], 'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...], }
NOTE that the key
'params'
is used to store a list of parameter settings dict for all the parameter candidates.The
mean_fit_time
,std_fit_time
,mean_score_time
andstd_score_time
are all in seconds.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.
best_score_ : float
Score of best_estimator on the left out data.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
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_
).scorer_ : function
Scorer function used on the held out data to choose the best parameters for the model.
n_splits_ : int
The number of crossvalidation splits (folds/iterations).
See also
GridSearchCV
 Does exhaustive search over a grid of parameters.
ParameterSampler
 A generator over parameter settins, constructed from param_distributions.
Notes
The parameters selected are those that maximize the score of the heldout 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.
Methods
decision_function
(*args, **kwargs)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
(*args, **kwargs)Call inverse_transform on the estimator with the best found params. predict
(*args, **kwargs)Call predict on the estimator with the best found parameters. predict_log_proba
(*args, **kwargs)Call predict_log_proba on the estimator with the best found parameters. predict_proba
(*args, **kwargs)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
(*args, **kwargs)Call transform on the estimator with the best found parameters. 
__init__
(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True)[source]¶

decision_function
(*args, **kwargs)[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
(*args, **kwargs)[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
(*args, **kwargs)[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
(*args, **kwargs)[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
(*args, **kwargs)[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 :