xgboost_classifier =============================================================================== .. py:module:: evalml.pipelines.components.estimators.classifiers.xgboost_classifier .. autoapi-nested-parse:: XGBoost Classifier. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: XGBoostClassifier(eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_seed=0, eval_metric='logloss', n_jobs=12, **kwargs) XGBoost Classifier. :param eta: Boosting learning rate. Defaults to 0.1. :type eta: float :param max_depth: Maximum tree depth for base learners. Defaults to 6. :type max_depth: int :param min_child_weight: Minimum sum of instance weight (hessian) needed in a child. Defaults to 1.0 :type min_child_weight: float :param n_estimators: Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100. :type n_estimators: int :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int :param n_jobs: Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to 12. :type n_jobs: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - { "eta": Real(0.000001, 1), "max_depth": Integer(1, 10), "min_child_weight": Real(1, 10), "n_estimators": Integer(1, 1000),} * - **model_family** - ModelFamily.XGBOOST * - **modifies_features** - True * - **modifies_target** - False * - **name** - XGBoost Classifier * - **SEED_MAX** - None * - **SEED_MIN** - None * - **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.xgboost_classifier.XGBoostClassifier.clone evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.describe evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.fit evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.get_prediction_intervals evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.load evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.parameters evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.predict evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.xgboost_classifier.XGBoostClassifier.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 XGBoost classifier. .. py:method:: fit(self, X, y=None) Fits XGBoost 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) -> 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] :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 XGBoost classifier. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.DataFrame .. py:method:: predict_proba(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.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