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