One Class SVM

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 is ‘auto’ then 1/n_features will be used instead.

• 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

  The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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

Ports:

Outputs:

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’. If gamma is ‘auto’ then 1/n_features will be used instead.

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

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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