Source code for evalml.pipelines.components.estimators.classifiers.rf_classifier

"""Random Forest Classifier."""

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. Args: n_estimators (float): The number of trees in the forest. Defaults to 100. max_depth (int): Maximum tree depth for base learners. Defaults to 6. n_jobs (int or None): Number of jobs to run in parallel. -1 uses all processes. Defaults to -1. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Random Forest Classifier" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 10), } """{ "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 10), }""" model_family = ModelFamily.RANDOM_FOREST """ModelFamily.RANDOM_FOREST""" supported_problem_types = [ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ] """[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ]""" 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_classifier = SKRandomForestClassifier(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=rf_classifier, random_seed=random_seed, )