scikits.learn.svm.LinearSVC

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

Linear Support Vector Classification.

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.

eps: float, optional :

precision for stopping criteria

multi_class: boolean, optional :

perform multi-class SVM by Cramer and Singer. If active, options loss, penalty and dual will be ignored.

Attributes

support_	array-like, shape = [nSV, n_features]	Support vectors.
dual_coef_	array, shape = [n_class-1, nSV]	Coefficients of the support vector in the decision function.
coef_	array, shape = [n_class-1, n_features]	Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.
intercept_	array, shape = [n_class-1]	Constants in decision function.

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)

Fit the model according to the given training data and parameters.

Parameters :

X : array-like, shape = [nsamples, nfeatures]

Training vector, where nsamples in the number of samples and nfeatures is the number of features.

Y : array, shape = [nsamples]

Target vector relative to X

Returns :

self : object

Returns self.

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 : array-like, shape = [n_samples, n_features] Returns :C : array, shape = [nsample]

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