2018-12-02 （SVM classification)

Classifier

SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset.

SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical

formulations. LinearSVC does not accept keyword kernel, as this is assumed to be linear. It also lacks some of the members ofSVC and NuSVC, like support_.

Input

Array X: an array X of size [n_samples,n_features] holding the training samples

Array y: array y of class labels (strings or integers), size [n_samples]

Example (Training)

>>> from sklearn import svm

>>> X = [[0, 0], [1, 1]]

>>> y = [0, 1]

>>> clf = svm.SVC(gamma='scale')

>>> clf.fit(X, y) 

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)

Example (Predicting)

>>>clf.predict([[2., 2.]])

Note

SVMs decision function depends on some subset of the training data, called the support vectors. Some properties of these support vectors can be found in members support_vectors_, support_ and n_support

>>> # get support vectors

>>> clf.support_vectors_array([[0., 0.], [1., 1.]])

>>> # get indices of support vectors

>>> clf.support_ array([0, 1]...)

>>> # get number of support vectors for each class

>>> clf.n_support_ array([1, 1]...)