8.26.1.6. sklearn.svm.OneClassSVM

class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False)

Unsupervised Outliers Detection.

Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm.

Parameters :

kernel : string, optional

Specifies the kernel type to be used in the algorithm. Can be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.

nu : float, optional

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 : int, optional

Degree of kernel function. Significant only in poly, rbf, sigmoid.

gamma : float, optional (default=0.0)

kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features will be taken.

coef0 : float, optional

Independent term in kernel function. It is only significant in poly/sigmoid.

tol: float, optional :

Tolerance for stopping criterion.

shrinking: boolean, optional :

Whether to use the shrinking heuristic.

cache_size: float, optional :

Specify the size of the kernel cache (in MB)

verbose : bool, default: False

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

Attributes

support_ array-like, shape = [n_SV] Index of support vectors.
support_vectors_ array-like, shape = [nSV, n_features] Support vectors.
dual_coef_ array, shape = [n_classes-1, n_SV] Coefficient of the support vector in the decision function.
coef_ array, shape = [n_classes-1, n_features]

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

coef_ is readonly property derived from dual_coef_ and support_vectors_

intercept_ array, shape = [n_classes-1] Constants in decision function.

Methods

decision_function(X) Distance of the samples X to the separating hyperplane.
fit(X[, sample_weight]) Detects the soft boundary of the set of samples X.
get_params([deep]) Get parameters for the estimator
predict(X) Perform classification or regression samples in X.
set_params(**params) Set the parameters of the estimator.
__init__(kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False)
decision_function(X)

Distance of the samples X to the separating hyperplane.

Parameters :

X : array-like, shape = [n_samples, n_features]

Returns :

X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]

Returns the decision function of the sample for each class in the model.

fit(X, sample_weight=None, **params)

Detects the soft boundary of the set of samples X.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Set of samples, where n_samples is the number of samples and n_features is the number of features.

Returns :

self : object

Returns self.

Notes

If X is not a C-ordered contiguous array it is copied.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Perform classification or regression samples in X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.

For an one-class model, +1 or -1 is returned.

Parameters :X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns :y_pred : array, shape = [n_samples]
set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :
Previous
Next