.. _`One Class Support Vector Machines`:

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

One Class Support Vector Machines
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.. image:: outliers.svg
   :width: 48


Unsupervised outlier detection based on support vector machines




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 vectors in the decision function.


    **intercept_**
        Constant in the decision function.


    **support_**
        Number of support vectors for each class.


    **support_vectors_**
        Support vectors.


Definition
::::::::::


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

    **model**  model
        Model




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

    **Independent kernel function term** (coef0)
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.
    **Polynomial kernel degree** (degree)
        Degree of the polynomial kernel function ('poly').
        Must be non-negative. Ignored by all other kernels.
    **Kernel coefficient** (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.
    **Hard iteration limit** (max_iter)
        Hard limit on iterations within solver, or -1 for no limit.
    **Upper/lower fraction bound** (nu)
        An upper bound on the fraction of training
        errors and a lower bound of the fraction of support
        vectors. Should be in the interval (0, 1]. By default 0.5
        will be taken.
    **Use shrinking heuristic** (shrinking)
        Whether to use the shrinking heuristic.
        See the User Guide <shrinking_svm>.
    **Tolerance** (tol)
        Tolerance for stopping criterion.









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

.. automodule:: node_svc
    :noindex:

.. class:: OneClassSVM
    :noindex: