One Class SVM

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

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.
Inputs:
Outputs:
model : model

Model
Output ports:
model:

model

Model
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.

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]