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, ProblemTypes.TIME_SERIES_REGRESSION]
[docs] def __init__(self, n_estimators=100, max_depth=6, n_jobs=-1, random_seed=0, **kwargs):
parameters = {"n_estimators": n_estimators,
"max_depth": max_depth,
"n_jobs": n_jobs}
parameters.update(kwargs)
rf_regressor = SKRandomForestRegressor(random_state=random_seed,
**parameters)
super().__init__(parameters=parameters,
component_obj=rf_regressor,
random_seed=random_seed)