One Class SVM

../../../../_images/outliers.svg

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’.

    Current default is ‘auto’ which uses 1 / n_features, if gamma='scale' is passed then it uses 1 / (n_features * X.var()) 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.

  • 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

    Ignored.

    Deprecated since version 0.20: random_state has been deprecated in 0.20 and will be removed in 0.22.

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:
modelmodel

Model

Definition

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_svc.OneClassSVM[source]