.. _`Epsilon Support Vector Regression`:

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

Epsilon Support Vector Regression
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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


Support vector machine based regressor (SVR)

:Configuration:


  - *C*

    Penalty parameter C of the error term.


  - *kernel*

    Specifies the kernel type to be used in the algorithm.
    It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
    a callable.
    If none is given, 'rbf' will be used. If a callable is given it is
    used to precompute the kernel matrix.


  - *gamma*

    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
    If gamma is 'auto' then 1/n_features will be used instead.


  - *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.


  - *coef0*

    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.


  - *tol*

    Tolerance for stopping criterion.


  - *degree*

    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.


  - *shrinking*

    Whether to use the shrinking heuristic.


  - *max_iter*

    Hard limit on iterations within solver, or -1 for no limit.




:Attributes:


  - *support_*

    Indices of support vectors.


  - *support_vectors_*

    Support vectors.


  - *dual_coef_*

    Coefficients of the support vector in the decision function.


  - *intercept_*

    Constants in decision function.


  - *coef_*

    Coefficients of the support vector in the decision function.




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **C**
        Penalty parameter C of the error term.
    **kernel**
        Specifies the kernel type to be used in the algorithm.
        It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
        a callable.
        If none is given, 'rbf' will be used. If a callable is given it is
        used to precompute the kernel matrix.
    **gamma**
        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
        If gamma is 'auto' then 1/n_features will be used instead.
    **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.
    **coef0**
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.
    **tol**
        Tolerance for stopping criterion.
    **degree**
        Degree of the polynomial kernel function ('poly').
        Ignored by all other kernels.
    **shrinking**
        Whether to use the shrinking heuristic.
    **max_iter**
        Hard limit on iterations within solver, or -1 for no limit.

.. automodule:: node_regression

.. class:: SupportVectorRegression