class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=None, iid=’warn’, refit=True, cv=’warn’, verbose=0, pre_dispatch=‘2*n_jobs’, random_state=None, error_score=’raisedeprecating’, return_train_score=’warn’)
[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.
Note that before SciPy 0.16, the scipy.stats.distributions
do not accept a custom RNG instance and always use the singleton RNG from numpy.random
. Hence setting random_state
will not guarantee a deterministic iteration whenever scipy.stats
distributions are used to define the parameter search space.
Read more in the User Guide.
Parameters: 
 

Attributes: 

See also
GridSearchCV
ParameterSampler
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
.
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_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=None, iid=’warn’, refit=True, cv=’warn’, verbose=0, pre_dispatch=‘2*n_jobs’, random_state=None, 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.RandomizedSearchCV
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html