One Class Support Vector Machines

Unsupervised outlier detection based on support vector machines

Documentation

Attributes

coef_

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_.

dual_coef_

Coefficients of the support vectors in the decision function.

intercept_

Constant in the decision function.

support_

Number of support vectors for each class.

support_vectors_

Support vectors.

Definition

Output ports

model model

Model

Configuration

Independent kernel function term (coef0)

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

Polynomial kernel degree (degree)

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

Kernel coefficient (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’.

Kernel (kernel)

Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

Hard iteration limit (max_iter)

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

Upper/lower fraction bound (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.

Use shrinking heuristic (shrinking)

Whether to use the shrinking heuristic. See the User Guide <shrinking_svm>.

Tolerance (tol)

Tolerance for stopping criterion.

Implementation

class node_svc.OneClassSVM