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)[source]

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

Inheritance diagram of DecisionTreeRegressor