Class LinearSVCScikitsLearnNode

Linear Support Vector Classification, Sparse Version This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. 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.

intercept_scaling : float, default: 1

when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased

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

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.

See also

SVC

References

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

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Returns: bool

A boolean indicating whether the node can be trained.

Overrides: Node.is_trainable

Predict target values of X according to the fitted model. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : sparse matrix, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

Fit the model using X, y as training data. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. 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.

Overrides: Node.stop_training