.. _`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'.

    Current default is 'auto' which uses 1 / n_features,
    if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.std())
    as value of gamma. The current default of gamma, 'auto', will change
    to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of
    'auto' is used as a default indicating that no explicit value of gamma
    was passed.


  - *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.




*Input ports*:


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



**C** (C)
    Penalty parameter C of the error term.
**kernel** (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** (gamma)
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

    Current default is 'auto' which uses 1 / n_features,
    if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.std())
    as value of gamma. The current default of gamma, 'auto', will change
    to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of
    'auto' is used as a default indicating that no explicit value of gamma
    was passed.
**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.
**coef0** (coef0)
    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.
**tol** (tol)
    Tolerance for stopping criterion.
**degree** (degree)
    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.
**shrinking** (shrinking)
    Whether to use the shrinking heuristic.
**max_iter** (max_iter)
    Hard limit on iterations within solver, or -1 for no limit.

.. automodule:: node_regression

.. class:: SupportVectorRegression