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

"""Decision Tree Regressor."""

from sklearn.tree import DecisionTreeRegressor as SKDecisionTreeRegressor
from import Integer

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes

[docs]class DecisionTreeRegressor(Estimator): """Decision Tree Regressor. Args: criterion ({"squared_error", "friedman_mse", "absolute_error", "poisson"}): The function to measure the quality of a split. Supported criteria are: - "squared_error" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node - "friedman_mse", which uses mean squared error with Friedman"s improvement score for potential splits - "absolute_error" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, - "poisson" which uses reduction in Poisson deviance to find splits. max_features (int, float or {"sqrt", "log2"}): The number of features to consider when looking for the best split: - If int, then consider max_features features at each split. - If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split. - If "sqrt", then max_features=sqrt(n_features). - If "log2", then max_features=log2(n_features). - If None, then max_features = n_features. The search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features. max_depth (int): The maximum depth of the tree. Defaults to 6. min_samples_split (int or float): The minimum number of samples required to split an internal node: - If int, then consider min_samples_split as the minimum number. - If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split. Defaults to 2. min_weight_fraction_leaf (float): The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Defaults to 0.0. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Decision Tree Regressor" hyperparameter_ranges = { "criterion": ["squared_error", "friedman_mse", "absolute_error"], "max_features": ["sqrt", "log2"], "max_depth": Integer(4, 10), } """{ "criterion": ["squared_error", "friedman_mse", "absolute_error"], "max_features": ["sqrt", "log2"], "max_depth": Integer(4, 10), }""" model_family = ModelFamily.DECISION_TREE """ModelFamily.DECISION_TREE""" supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION, ]""" def __init__( self, criterion="squared_error", max_features="sqrt", max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, random_seed=0, **kwargs, ): parameters = { "criterion": criterion, "max_features": max_features, "max_depth": max_depth, "min_samples_split": min_samples_split, "min_weight_fraction_leaf": min_weight_fraction_leaf, } parameters.update(kwargs) dt_regressor = SKDecisionTreeRegressor(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=dt_regressor, random_seed=random_seed, )