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

from sklearn.ensemble import ExtraTreesRegressor as SKExtraTreesRegressor
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 ExtraTreesRegressor(Estimator): """Extra Trees Regressor.""" name = "Extra Trees Regressor" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_features": ["auto", "sqrt", "log2"], "max_depth": Integer(4, 10), } model_family = ModelFamily.EXTRA_TREES supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ]
[docs] def __init__( self, 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 ): parameters = { "n_estimators": n_estimators, "max_features": max_features, "max_depth": max_depth, "min_samples_split": min_samples_split, "min_weight_fraction_leaf": min_weight_fraction_leaf, "n_jobs": n_jobs, } parameters.update(kwargs) et_regressor = SKExtraTreesRegressor(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=et_regressor, random_seed=random_seed )