evalml.pipelines.components.CatBoostRegressor

class evalml.pipelines.components.CatBoostRegressor(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False, allow_writing_files=False, random_state=None, random_seed=0, **kwargs)

CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.

For more information, check out https://catboost.ai/

name = 'CatBoost Regressor'

model_family = 'catboost'

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

hyperparameter_ranges = {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=4, high=100, prior='uniform', transform='identity')}

default_parameters = {'allow_writing_files': False, 'bootstrap_type': None, 'eta': 0.03, 'max_depth': 6, 'n_estimators': 10, 'silent': False}

predict_uses_y = False

Instance attributes

feature_importance	Return an attribute of instance, which is of type owner.
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.SVMClassifier.save evalml.pipelines.components.CatBoostRegressor.__init__