.. _`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**

Support vector machine based regressor (SVR)

*Configuration*:


  - *C*

    Regularization parameter. The strength of the regularization is
    inversely proportional to C. Must be strictly positive.
    The penalty is a squared l2 penalty.


  - *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.


  - *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.


  - *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.

    .. versionchanged:: 0.22
       The default value of ``gamma`` changed from 'auto' to 'scale'.


  - *degree*

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


  - *coef0*

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


  - *max_iter*

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


  - *tol*

    Tolerance for stopping criterion.


  - *shrinking*

    Whether to use the shrinking heuristic.
    See the User Guide <shrinking_svm>.




*Attributes*:


  - *support_*

    Number of support vectors for each class.


  - *support_vectors_*

    Support vectors.


  - *dual_coef_*

    Coefficients of the support vector in the decision function.


  - *intercept_*

    Constants in decision function.


  - *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_`.




*Input ports*:


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


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_regression
    :noindex:

.. class:: SupportVectorRegression
    :noindex: