.. _`Linear Regression`:

.. _`org.sysess.sympathy.machinelearning.linearregression`:

Linear Regression
~~~~~~~~~~~~~~~~~

.. image:: linear_regression.svg
   :width: 48


Ordinary linear regression

:Configuration:


  - *fit_intercept*

    whether to calculate the intercept for this model. If set
    to False, no intercept will be used in calculations
    (e.g. data is expected to be already centered).


  - *normalize*

    This parameter is ignored when ``fit_intercept`` is set to False.
    If True, the regressors X will be normalized before regression by
    subtracting the mean and dividing by the l2-norm.
    If you wish to standardize, please use
    :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on
    an estimator with ``normalize=False``.


  - *n_jobs*

    The number of jobs to use for the computation.
    If -1 all CPUs are used. This will only provide speedup for
    n_targets > 1 and sufficient large problems.




:Attributes:


  - *coef_*

    Estimated coefficients for the linear regression problem.
    If multiple targets are passed during the fit (y 2D), this
    is a 2D array of shape (n_targets, n_features), while if only
    one target is passed, this is a 1D array of length n_features.


  - *intercept_*

    Independent term in the linear model.


  - *residues_*





:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **fit_intercept**
        whether to calculate the intercept for this model. If set
        to False, no intercept will be used in calculations
        (e.g. data is expected to be already centered).
    **normalize**
        This parameter is ignored when ``fit_intercept`` is set to False.
        If True, the regressors X will be normalized before regression by
        subtracting the mean and dividing by the l2-norm.
        If you wish to standardize, please use
        :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on
        an estimator with ``normalize=False``.
    **n_jobs**
        The number of jobs to use for the computation.
        If -1 all CPUs are used. This will only provide speedup for
        n_targets > 1 and sufficient large problems.

.. automodule:: node_regression

.. class:: LinearRegression