.. _`Epsilon Support Vector Regression`:

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

Epsilon Support Vector Regression
`````````````````````````````````

.. image:: svm.svg
   :width: 48


Support vector machine based regressor (SVR)




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

Attributes
==========

    **coef_**
        Weights assigned to the features (coefficients in the primal
        problem). This is only available in the case of a linear kernel.

        `coef_` is readonly property derived from `dual_coef_` and
        `support_vectors_`.


    **dual_coef_**
        Coefficients of the support vector in the decision function.


    **intercept_**
        Constants in decision function.


    **support_**
        Number of support vectors.


    **support_vectors_**
        Support vectors.


Definition
::::::::::


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

    **model**  model
        Model




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

    **Penalty** (C)
        Regularization parameter. The strength of the regularization is
        inversely proportional to C. Must be strictly positive.
        The penalty is a squared l2 penalty.
    **Independent term in kernel function** (coef0)
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.
    **Polynomial Degree** (degree)
        Degree of the polynomial kernel function ('poly').
        Must be non-negative. Ignored by all other kernels.
    **Epsilon** (epsilon)
        Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
        within which no penalty is associated in the training loss function
        with points predicted within a distance epsilon from the actual
        value. Must be non-negative.
    **Gamma** (gamma)
        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

        - if ``gamma='scale'`` (default) is passed then it uses
          1 / (n_features * X.var()) as value of gamma,
        - if 'auto', uses 1 / n_features
        - if float, must be non-negative.

        .. versionchanged:: 0.22
           The default value of ``gamma`` changed from 'auto' to 'scale'.
    **Kernel** (kernel)
        Specifies the kernel type to be used in the algorithm.
        If none is given, 'rbf' will be used. If a callable is given it is
        used to precompute the kernel matrix.
    **Maximum iterations** (max_iter)
        Hard limit on iterations within solver, or -1 for no limit.
    **Shrinking** (shrinking)
        Whether to use the shrinking heuristic.
        See the User Guide <shrinking_svm>.
    **Tolerance** (tol)
        Tolerance for stopping criterion.









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

.. automodule:: node_regression
    :noindex:

.. class:: SupportVectorRegression
    :noindex: