scikits.learn.svm.sparse.LinearSVC

class scikits.learn.svm.sparse.LinearSVC(penalty='l2', loss='l2', dual=True, eps=0.0001, C=1.0, multi_class=False, fit_intercept=True)

Linear Support Vector Classification, Sparse Version

Similar to SVC with parameter kernel=’linear’, but uses internally liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should be faster for huge datasets.

Parameters :

loss : string, ‘l1’ or ‘l2’ (default ‘l2’)

Specifies the loss function. With ‘l1’ it is the standard SVM loss (a.k.a. hinge Loss) while with ‘l2’ it is the squared loss. (a.k.a. squared hinge Loss)

penalty : string, ‘l1’ or ‘l2’ (default ‘l2’)

Specifies the norm used in the penalization. The ‘l2’ penalty is the standard used in SVC. The ‘l1’ leads to coef_ vectors that are sparse.

dual : bool, (default True)

Select the algorithm to either solve the dual or primal optimization problem.

Notes

Some features of liblinear are still not wrapped, like the Cramer & Singer algorithm.

References

LIBLINEAR – A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/

Attributes

Methods

fit
predict
predict_proba
score

__init__(penalty='l2', loss='l2', dual=True, eps=0.0001, C=1.0, multi_class=False, fit_intercept=True)

fit(X, Y, **params)

Parameters :

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

Training vector, where n_samples in the number of samples and n_features is the number of features.

Y : array, shape = [n_samples]

Target vector relative to X

score(X, y)

Returns the mean error rate on the given test data and labels.

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float