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.

An 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.

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.

Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.

Strategy to evaluate the performance of the cross-validated model on the test set.

If scoring represents a single score, one can use:

If scoring represents multiple scores, one can use:

See Specifying multiple metrics for evaluation for an example.

If None, the estimator’s score method is used.

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

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.

Determines the cross-validation splitting strategy. Possible inputs for cv are:

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. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

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.

Controls the verbosity: the higher, the more messages.

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:

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 .

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.

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

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’

‘rbf’

‘rbf’

will be represented by a cv_results_ dict of:

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.)

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.

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.

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.

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 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.

The number of cross-validation splits (folds/iterations).

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.

Whether or not the scorers compute several metrics.

Class labels.

Number of features seen during fit .

Names of features seen during fit . Only defined if best_estimator_ is defined (see the documentation for the refit parameter for more details) and that best_estimator_ exposes feature_names_in_ when fit.

New in version 1.0.

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_metadata_routing()
Get metadata routing of this object.
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])
Return 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_fit_request(*[, groups])
Request metadata passed to the fit method.
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.
Returns:
y_scorendarray of shape (n_samples,) or (n_samples, n_classes)                 or (n_samples, n_classes * (n_classes-1) / 2)
Result of the decision function for X based on the estimator with
the best found parameters.
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.
If a fit parameter is an array-like whose length is equal to
num_samples then it will be split across CV groups along with X
and y. For example, the sample_weight parameter is split
because len(sample_weights) = len(X).
Returns:
selfobject
Instance of fitted estimator.
get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing
mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating
routing information.
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.
Returns:
X{ndarray, sparse matrix} of shape (n_samples, n_features)
Result of the inverse_transform function for Xt based on the
estimator with the best found 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:
Xindexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
Returns:
y_predndarray of shape (n_samples,)
The predicted labels or values for X based on the estimator with
the best found 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:
Xindexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
Returns:
y_predndarray of shape (n_samples,) or (n_samples, n_classes)
Predicted class log-probabilities for X based on the estimator
with the best found parameters. The order of the classes
corresponds to that in the fitted attribute classes_.
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.
Returns:
y_predndarray of shape (n_samples,) or (n_samples, n_classes)
Predicted class probabilities for X based on the estimator with
the best found parameters. The order of the classes corresponds
to that in the fitted attribute classes_.
score(X, y=None)[source]¶
Return 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
The score defined by scoring if provided, and the
best_estimator_.score method otherwise.
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,)
The best_estimator_.score_samples method.
set_fit_request(*, groups: Union[bool, None, str] = '$UNCHANGED$') → RandomizedSearchCV[source]¶
Request metadata passed to the fit method.
Note that this method is only relevant if
enable_metadata_routing=True (see sklearn.set_config).
Please see User Guide on how the routing
mechanism works.
The options for each parameter are:
True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (sklearn.utils.metadata_routing.UNCHANGED) retains the
existing request. This allows you to change the request for some
parameters and not others.
New in version 1.3.
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
Pipeline. Otherwise it has no effect.
Parameters:
groupsstr, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for groups parameter in fit.
Returns:
selfobject
The updated object.
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.
Returns:
Xt{ndarray, sparse matrix} of shape (n_samples, n_features)
X transformed in the new space based on the estimator with
the best found parameters.
Examples using sklearn.model_selection.RandomizedSearchCV¶
Release Highlights for scikit-learn 0.24
  
Release Highlights for scikit-learn 0.24
Faces recognition example using eigenfaces and SVMs
  
Faces recognition example using eigenfaces and SVMs
Comparison of kernel ridge and Gaussian process regression
  
Comparison of kernel ridge and Gaussian process regression
Comparing randomized search and grid search for hyperparameter estimation
  
Comparing randomized search and grid search for hyperparameter estimation
Sample pipeline for text feature extraction and evaluation
  
Sample pipeline for text feature extraction and evaluation
Column Transformer with Mixed Types
  
Column Transformer with Mixed Types