evalml.pipelines.components.CatBoostClassifier

class evalml.pipelines.components.CatBoostClassifier(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=True, allow_writing_files=False, random_seed=0, **kwargs)[source]

CatBoost Classifier, a classifier 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 Classifier'
model_family = 'catboost'
supported_problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.MULTICLASS: 'multiclass'>, <ProblemTypes.TIME_SERIES_BINARY: 'time series binary'>, <ProblemTypes.TIME_SERIES_MULTICLASS: 'time series multiclass'>]
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': True}
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

Inheritance diagram of CatBoostClassifier