.. _`Linear Regression`:

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

Linear Regression
`````````````````

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


Ordinary linear regression




Documentation
:::::::::::::

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. Set to 0.0 if
        `fit_intercept = False`.


    **residues_**


Definition
::::::::::


Output ports
============

    **model**  model
        Model




Configuration
=============

    **Fit intercept** (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).
    **Number of jobs** (n_jobs)
        The number of jobs to use for the computation. This will only provide
        speedup in case of sufficiently large problems, that is if firstly
        `n_targets > 1` and secondly `X` is sparse or if `positive` is set
        to `True`. ``None`` means 1 unless in a
        :obj:`joblib.parallel_backend` context. ``-1`` means using all
        processors. See n_jobs for more details.









Implementation
==============

.. automodule:: node_regression
    :noindex:

.. class:: LinearRegression
    :noindex: