Machine Learning, Svm.
margin. Margin = m So now comes the next question, what causes SVM to maximize the margin ‘m’ ? The answer lies in optimizing the cost/ loss function that was discussed in Part #1. As we noted from the plot in Part#1, the hinge-loss becomes 0, when the ‘predicted y’ ≥ 1. And ‘predicted y’ as seen