evalml.pipelines.components.DecisionTreeRegressor

class evalml.pipelines.components.DecisionTreeRegressor(criterion='mse', max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, random_state=0, **kwargs)

Decision Tree Regressor.

name = 'Decision Tree Regressor'

model_family = 'decision_tree'

supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>, <ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]

hyperparameter_ranges = {'criterion': ['mse', 'friedman_mse', 'mae'], 'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'max_features': ['auto', 'sqrt', 'log2']}

default_parameters = {'criterion': 'mse', 'max_depth': 6, 'max_features': 'auto', 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0}

predict_uses_y = False

Instance attributes

feature_importance	Returns importance associated with each feature.
needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters and random state.
describe	Describe a component and its parameters
fit	Fits component to data
load	Loads component at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves component at file path

Class Inheritance

evalml.pipelines.components.StackedEnsembleRegressor.save evalml.pipelines.components.DecisionTreeRegressor.__init__