Source code for evalml.pipelines.components.estimators.regressors.rf_regressor

from sklearn.ensemble import RandomForestRegressor as SKRandomForestRegressor
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 RandomForestRegressor(Estimator): """Random Forest Regressor""" name = "Random Forest Regressor" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 32), } model_family = ModelFamily.RANDOM_FOREST supported_problem_types = [ProblemTypes.REGRESSION]
[docs] def __init__(self, n_estimators=100, max_depth=6, n_jobs=-1, random_state=0): parameters = {"n_estimators": n_estimators, "max_depth": max_depth} rf_regressor = SKRandomForestRegressor(random_state=random_state, n_estimators=n_estimators, max_depth=max_depth, n_jobs=n_jobs) super().__init__(parameters=parameters, component_obj=rf_regressor, random_state=random_state)
@property def feature_importances(self): return self._component_obj.feature_importances_