linear_regressor
============================================================================

.. py:module:: evalml.pipelines.components.estimators.regressors.linear_regressor

.. autoapi-nested-parse::

   Linear Regressor.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: LinearRegressor(fit_intercept=True, normalize=False, n_jobs=-1, random_seed=0, **kwargs)



   Linear Regressor.

   :param fit_intercept: Whether to calculate the intercept for this model.
                         If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).
                         Defaults to True.
   :type fit_intercept: boolean
   :param normalize: If True, the regressors will be normalized before regression
                     by subtracting the mean and dividing by the l2-norm.
                     This parameter is ignored when fit_intercept is set to False. Defaults to False.
   :type normalize: boolean
   :param n_jobs: Number of jobs to run in parallel. -1 uses all threads. 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**
        - {    "fit_intercept": [True, False],    "normalize": [True, False]}
      * - **model_family**
        - ModelFamily.LINEAR_MODEL
      * - **modifies_features**
        - True
      * - **modifies_target**
        - False
      * - **name**
        - Linear Regressor
      * - **supported_problem_types**
        - [    ProblemTypes.REGRESSION,    ProblemTypes.TIME_SERIES_REGRESSION,]
      * - **training_only**
        - False


   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.clone
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.default_parameters
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.describe
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.feature_importance
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.fit
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.load
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.needs_fitting
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.parameters
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.predict
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.predict_proba
      evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor.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 linear 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