One Class SVM

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

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