evalml.pipelines.components.CatBoostRegressor

class evalml.pipelines.components.CatBoostRegressor(n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None, random_state=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'>]

hyperparameter_ranges = {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=1, high=16, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}

default_parameters = {'bootstrap_type': None, 'eta': 0.03, 'max_depth': 6, 'n_estimators': 1000}

Instance attributes

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

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters
describe	Describe a component and its parameters
fit	Build a model
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.