sklearn.utils.testing.all_estimators — scikit-learn 0.20.4 documentation (original) (raw)

sklearn.utils.testing. all_estimators(include_meta_estimators=False, include_other=False, type_filter=None, include_dont_test=False)[source]¶

Get a list of all estimators from sklearn.

This function crawls the module and gets all classes that inherit from BaseEstimator. Classes that are defined in test-modules are not included. By default meta_estimators such as GridSearchCV are also not included.

Parameters:	include_meta_estimators : boolean, default=False Whether to include meta-estimators that can be constructed using an estimator as their first argument. These are currently BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier, OneVsRestClassifier, RFE, RFECV. include_other : boolean, default=False Wether to include meta-estimators that are somehow special and can not be default-constructed sensibly. These are currently Pipeline, FeatureUnion and GridSearchCV type_filter : string, list of string, or None, default=None Which kind of estimators should be returned. If None, no filter is applied and all estimators are returned. Possible values are ‘classifier’, ‘regressor’, ‘cluster’ and ‘transformer’ to get estimators only of these specific types, or a list of these to get the estimators that fit at least one of the types. include_dont_test : boolean, default=False Whether to include “special” label estimator or test processors.
Returns:	estimators : list of tuples List of (name, class), where name is the class name as string and class is the actuall type of the class.

Parameters:

include_meta_estimators : boolean, default=False Whether to include meta-estimators that can be constructed using an estimator as their first argument. These are currently BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier, OneVsRestClassifier, RFE, RFECV. include_other : boolean, default=False Wether to include meta-estimators that are somehow special and can not be default-constructed sensibly. These are currently Pipeline, FeatureUnion and GridSearchCV type_filter : string, list of string, or None, default=None Which kind of estimators should be returned. If None, no filter is applied and all estimators are returned. Possible values are ‘classifier’, ‘regressor’, ‘cluster’ and ‘transformer’ to get estimators only of these specific types, or a list of these to get the estimators that fit at least one of the types. include_dont_test : boolean, default=False Whether to include “special” label estimator or test processors.

Returns:

estimators : list of tuples List of (name, class), where name is the class name as string and class is the actuall type of the class.