evalml.pipelines.components.RandomForestClassifier

class evalml.pipelines.components.RandomForestClassifier(n_estimators=100, max_depth=6, n_jobs=-1, random_state=0)

Random Forest Classifier

name = 'Random Forest Classifier'

model_family = 'random_forest'

supported_problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.MULTICLASS: 'multiclass'>]

hyperparameter_ranges = {'max_depth': Integer(low=1, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}

Instance attributes

feature_importances

Returns feature importances.

Methods:

__init__	Initialize self.
describe	Describe a component and its parameters
fit	Fits component to data
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.