.. _`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. This will only provide
    speedup for n_targets > 1 and sufficient large problems.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.




*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_*





*Input ports*:


*Output ports*:
    **model** : model
        Model



**fit_intercept** (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** (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** (n_jobs)
    The number of jobs to use for the computation. This will only provide
    speedup for n_targets > 1 and sufficient large problems.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.

.. automodule:: node_regression

.. class:: LinearRegression