One Class Support Vector Machines

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.

  Changed in version 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. See the User Guide <shrinking_svm>.

• 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:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_svc.OneClassSVM