Machine Learning with Spark - Second Edition

Linear support vector machines

SVM is a powerful and popular technique for regression and classification. Unlike logistic regression, it is not a probabilistic model but predicts classes based on whether the model evaluation is positive or negative.

The SVM link function is the identity link, so the predicted outcome is as follows:

y = w^Tx

Hence, if the evaluation of wTx is greater than or equal to a threshold of 0, the SVM will assign the data point to class 1; otherwise, the SVM will assign it to class 0

(this threshold is a model parameter of SVM, and can be adjusted).

The loss function for SVM is known as the hinge loss and is defined as follows:

max(0, 1 - yw^Tx)

SVM is a maximum margin classifier--it tries to find a weight vector such ...