- sklearn.metrics.hinge_loss(y_true, pred_decision, pos_label=None, neg_label=None)¶
Average hinge loss (non-regularized)
Assuming labels in y_true are encoded with +1 and -1, when a prediction mistake is made, margin = y_true * pred_decision is always negative (since the signs disagree), implying 1 - margin is always greater than 1. The cumulated hinge loss is therefore an upper bound of the number of mistakes made by the classifier.
y_true : array, shape = [n_samples]
True target, consisting of integers of two values. The positive label must be greater than the negative label.
pred_decision : array, shape = [n_samples] or [n_samples, n_classes]
Predicted decisions, as output by decision_function (floats).
loss : float
[R160] Wikipedia entry on the Hinge loss
>>> from sklearn import svm >>> from sklearn.metrics import hinge_loss >>> X = [, ] >>> y = [-1, 1] >>> est = svm.LinearSVC(random_state=0) >>> est.fit(X, y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2', random_state=0, tol=0.0001, verbose=0) >>> pred_decision = est.decision_function([[-2], , [0.5]]) >>> pred_decision array([-2.18..., 2.36..., 0.09...]) >>> hinge_loss([-1, 1, 1], pred_decision) 0.30...