elasticnet_regressor ================================================================================ .. py:module:: evalml.pipelines.components.estimators.regressors.elasticnet_regressor .. autoapi-nested-parse:: Elastic Net Regressor. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: ElasticNetRegressor(alpha=0.0001, l1_ratio=0.15, max_iter=1000, normalize=False, random_seed=0, **kwargs) Elastic Net Regressor. :param alpha: Constant that multiplies the penalty terms. Defaults to 0.0001. :type alpha: float :param l1_ratio: The mixing parameter, with 0 <= l1_ratio <= 1. Only used if penalty='elasticnet'. Setting l1_ratio=0 is equivalent to using penalty='l2', while setting l1_ratio=1 is equivalent to using penalty='l1'. For 0 < l1_ratio <1, the penalty is a combination of L1 and L2. Defaults to 0.15. :type l1_ratio: float :param max_iter: The maximum number of iterations. Defaults to 1000. :type max_iter: int :param normalize: If True, the regressors will be normalized before regression by subtracting the mean and dividing by the l2-norm. Defaults to False. :type normalize: boolean :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** - { "alpha": Real(0, 1), "l1_ratio": Real(0, 1),} * - **model_family** - ModelFamily.LINEAR_MODEL * - **modifies_features** - True * - **modifies_target** - False * - **name** - Elastic Net Regressor * - **supported_problem_types** - [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.clone evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.default_parameters evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.describe evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.feature_importance evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.fit evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.load evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.needs_fitting evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.parameters evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.predict evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.predict_proba evalml.pipelines.components.estimators.regressors.elasticnet_regressor.ElasticNetRegressor.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 for fitted ElasticNet regressor. .. 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:: 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