Machine Learning, Svm, Loss Function, Data Science.
y’. To put this in a simple way, this defines the amount by which you want to penalize the mis-classified observations. So intuitively, the farther the ‘predicted y’ from the ‘actual y’, more should be the penalty and vice-versa. Given the amount of total error ( = sum of losses of for each