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’.
Current default is ‘auto’ which uses 1 / n_features, if
gamma='scale'
is passed then it uses 1 / (n_features * X.std()) as value of gamma. The current default of gamma, ‘auto’, will change to ‘scale’ in version 0.22. ‘auto_deprecated’, a deprecated version of ‘auto’ is used as a default indicating that no explicit value of gamma was passed.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
Ignored.
Deprecated since version 0.20:
random_state
has been deprecated in 0.20 and will be removed in 0.22.
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:
- model : model
- Model
- kernel (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 (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)
- Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
- gamma (gamma)
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.
Current default is ‘auto’ which uses 1 / n_features, if
gamma='scale'
is passed then it uses 1 / (n_features * X.std()) as value of gamma. The current default of gamma, ‘auto’, will change to ‘scale’ in version 0.22. ‘auto_deprecated’, a deprecated version of ‘auto’ is used as a default indicating that no explicit value of gamma was passed.- coef0 (coef0)
- Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
- shrinking (shrinking)
- Whether to use the shrinking heuristic.
- tol (tol)
- Tolerance for stopping criterion.
- max_iter (max_iter)
- Hard limit on iterations within solver, or -1 for no limit.
- random_state (random_state)
Ignored.
Deprecated since version 0.20:
random_state
has been deprecated in 0.20 and will be removed in 0.22.
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.