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.
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class
node_svc.
OneClassSVM
[source]¶ 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