9.1.8.5. sklearn.svm.sparse.OneClassSVM

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

NuSVR for sparse matrices (csr)

See sklearn.svm.NuSVC for a complete list of parameters

Notes

For best results, this accepts a matrix in csr format (scipy.sparse.csr), but should be able to convert from any array-like object (including other sparse representations).

Methods

decision_function(X)	Distance of the samples T to the separating hyperplane.
fit(X[, class_weight, sample_weight])
predict(T)	This function does classification or regression on an array of
predict_log_proba(T)	Compute the log likehoods each possible outcomes of samples in T.
predict_proba(X)	This function does classification or regression on a test vector 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, probability=False)

decision_function(X)

Distance of the samples T 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.

predict(T)

This function does classification or regression on an array of test vectors T.

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

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

Parameters :T : scipy.sparse.csr, shape = [n_samples, n_features] Returns :C : array, shape = [n_samples]

predict_log_proba(T)

Compute the log likehoods each possible outcomes of samples in T.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters :

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

Returns :

T : array-like, shape = [n_samples, n_classes]

Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.

predict_proba(X)

This function does classification or regression on a test vector X given a model with probability information.

Parameters :

X : scipy.sparse.csr, shape = [n_samples, n_features]

Returns :

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

Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.

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 :