6.1.2. 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.
See also
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] Weights 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 according to the given training data and parameters. 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 according to the given training data and parameters.
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-like, shape = [n_samples]
Target vector relative to X
class_weight : dict , {class_label
Weights associated with classes. If not given, all classes are supposed to have weight one.
Returns : self : object
Returns self.
- predict(X)¶
Predict target values of X according to the fitted model.
Parameters : X : array-like, 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