stacked_ensemble_base ==================================================================== .. py:module:: evalml.pipelines.components.ensemble.stacked_ensemble_base .. autoapi-nested-parse:: Stacked Ensemble Base. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: StackedEnsembleBase(final_estimator=None, n_jobs=-1, random_seed=0, **kwargs) Stacked Ensemble Base Class. :param final_estimator: The estimator used to combine the base estimators. :type final_estimator: Estimator or subclass :param n_jobs: Integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs greater than -1, (n_cpus + 1 + n_jobs) are used. Defaults to -1. - Note: there could be some multi-process errors thrown for values of `n_jobs != 1`. If this is the case, please use `n_jobs = 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 * - **model_family** - ModelFamily.ENSEMBLE * - **modifies_features** - True * - **modifies_target** - False * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.clone evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.default_parameters evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.describe evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.feature_importance evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.fit evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.load evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.name evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.needs_fitting evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.parameters evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.predict evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.predict_proba evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.save evalml.pipelines.components.ensemble.stacked_ensemble_base.StackedEnsembleBase.supported_problem_types .. 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 stacked ensemble classes. :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: Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor. .. py:method:: fit(self, X, y=None) Fits estimator 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, optional :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:: name(cls) :property: Returns string name of this component. .. 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 .. py:method:: supported_problem_types(cls) :property: Problem types this estimator supports.