.. _`One Class Support Vector Machines`:

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

One Class Support Vector Machines
=================================

.. image:: outliers.svg
   :width: 48


Unsupervised outlier detection based on support vector machines



**Documentation**

Unsupervised outlier detection based on support vector machines

*Configuration*:


  - *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.


  - *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.


  - *degree*

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


  - *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'.


  - *coef0*

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


  - *shrinking*

    Whether to use the shrinking heuristic.


  - *tol*

    Tolerance for stopping criterion.


  - *max_iter*

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


  - *random_state*





*Attributes*:


  - *support_*

    Indices of support vectors.


  - *support_vectors_*

    Support vectors.


  - *dual_coef_*

    Coefficients of the support vectors in the decision function.


  - *coef_*

    Coefficients of the support vectors in the decision function.


  - *intercept_*

    Constant in the decision function.




*Input ports*:


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


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_svc

.. class:: OneClassSVM