evalml.pipelines.components.ExtraTreesClassifier

class evalml.pipelines.components.ExtraTreesClassifier(n_estimators=100, max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, n_jobs=- 1, random_seed=0, **kwargs)

Extra Trees Classifier.

name = 'Extra Trees Classifier'

model_family = 'extra_trees'

supported_problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.MULTICLASS: 'multiclass'>, <ProblemTypes.TIME_SERIES_BINARY: 'time series binary'>, <ProblemTypes.TIME_SERIES_MULTICLASS: 'time series multiclass'>]

hyperparameter_ranges = {'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}

default_parameters = {'max_depth': 6, 'max_features': 'auto', 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': -1}

predict_uses_y = False

Instance attributes

feature_importance	Returns importance associated with each feature.
needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters and random state.
describe	Describe a component and its parameters
fit	Fits component to data
load	Loads component at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves component at file path

Class Inheritance

evalml.pipelines.components.ElasticNetClassifier.save evalml.pipelines.components.ExtraTreesClassifier.__init__