catboost_classifier ================================================================================ .. py:module:: evalml.pipelines.components.estimators.classifiers.catboost_classifier .. autoapi-nested-parse:: CatBoost Classifier, a classifier 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.classifiers.catboost_classifier.CatBoostClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: CatBoostClassifier(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=True, allow_writing_files=False, random_seed=0, n_jobs=-1, **kwargs) 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/ :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 Classifier * - **supported_problem_types** - [ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.clone evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.describe evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.fit evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.get_prediction_intervals evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.load evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.parameters evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.predict evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.save evalml.pipelines.components.estimators.classifiers.catboost_classifier.CatBoostClassifier.update_parameters .. 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 classifier. .. py:method:: fit(self, X, y=None) Fits CatBoost classifier 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:: get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) -> Dict[str, pandas.Series] Find the prediction intervals using the fitted regressor. This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: Target data. Ignored. :type y: pd.Series :param coverage: A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for. :type coverage: list[float] :param predictions: Optional list of predictions to use. If None, will generate predictions using `X`. :type predictions: pd.Series :returns: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. :rtype: dict :raises MethodPropertyNotFoundError: If the estimator does not support Time Series Regression as a problem type. .. 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 the fitted CatBoost classifier. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series .. py:method:: predict_proba(self, X) Make prediction probabilities using the fitted CatBoost classifier. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted probability values. :rtype: pd.DataFrame .. 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 .. py:method:: update_parameters(self, update_dict, reset_fit=True) Updates the parameter dictionary of the component. :param update_dict: A dict of parameters to update. :type update_dict: dict :param reset_fit: If True, will set `_is_fitted` to False. :type reset_fit: bool, optional