from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier from skopt.space import Integer from evalml.model_family import ModelFamily from evalml.pipelines.components.estimators import Estimator from evalml.problem_types import ProblemTypes [docs]class RandomForestClassifier(Estimator): """Random Forest Classifier.""" name = "Random Forest Classifier" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 10), } model_family = ModelFamily.RANDOM_FOREST supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS] [docs] def __init__(self, n_estimators=100, max_depth=6, n_jobs=-1, random_state=0, **kwargs): parameters = {"n_estimators": n_estimators, "max_depth": max_depth, "n_jobs": n_jobs} parameters.update(kwargs) rf_classifier = SKRandomForestClassifier(random_state=random_state, **parameters) super().__init__(parameters=parameters, component_obj=rf_classifier, random_state=random_state)