catboost_regressor ============================================================================== .. py:module:: evalml.pipelines.components.estimators.regressors.catboost_regressor .. autoapi-nested-parse:: CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: CatBoostRegressor(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False, allow_writing_files=False, random_seed=0, n_jobs=-1, **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/ :param n_estimators: The maximum number of trees to build. Defaults to 10. :type n_estimators: float :param eta: The learning rate. Defaults to 0.03. :type eta: float :param max_depth: The maximum tree depth for base learners. Defaults to 6. :type max_depth: int :param bootstrap_type: Defines the method for sampling the weights of objects. Available methods are 'Bayesian', 'Bernoulli', 'MVS'. Defaults to None. :type bootstrap_type: string :param silent: Whether to use the "silent" logging mode. Defaults to True. :type silent: boolean :param allow_writing_files: Whether to allow writing snapshot files while training. Defaults to False. :type allow_writing_files: boolean :param n_jobs: Number of jobs to run in parallel. -1 uses all processes. Defaults to -1. :type n_jobs: int or None :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - { "n_estimators": Integer(4, 100), "eta": Real(0.000001, 1), "max_depth": Integer(4, 10),} * - **model_family** - ModelFamily.CATBOOST * - **modifies_features** - True * - **modifies_target** - False * - **name** - CatBoost Regressor * - **supported_problem_types** - [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.clone evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.default_parameters evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.describe evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.feature_importance evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.fit evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.load evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.needs_fitting evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.parameters evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.predict evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.predict_proba evalml.pipelines.components.estimators.regressors.catboost_regressor.CatBoostRegressor.save .. py:method:: clone(self) Constructs a new component with the same parameters and random state. :returns: A new instance of this component with identical parameters and random state. .. py:method:: default_parameters(cls) Returns the default parameters for this component. Our convention is that Component.default_parameters == Component().parameters. :returns: Default parameters for this component. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: Feature importance of fitted CatBoost regressor. .. py:method:: fit(self, X, y=None) Fits CatBoost regressor component to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series :returns: self .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: needs_fitting(self) Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing. :returns: True. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X) Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int