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6.1.7.6. 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.

See also

SVC

Notes

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller eps parameter.

References

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

Attributes

coef_ array, shape = [n_features] if n_classes == 2 else [n_classes, n_features] Wiehgiths asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.
intercept_ array, shape = [1] if n_classes == 2 else [n_classes] constants in decision function

Methods

fit(X, y[, class_weight]) Fit the model using X, y as training data.
predict(X) Predict target values of X according to the fitted model.
predict_proba(T)
score(X, y) Returns the mean error rate on the given test data and labels.
__init__(penalty='l2', loss='l2', dual=True, eps=0.0001, C=1.0, multi_class=False, fit_intercept=True)
fit(X, y, class_weight={}, **params)

Fit the model using X, y as training data.

Parameters :

X : sparse matrix, 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

Returns :

self : object

Returns an instance of self.

predict(X)

Predict target values of X according to the fitted model.

Parameters :X : sparse matrix, shape = [n_samples, n_features]
Returns :C : array, shape = [n_samples]
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