 is non-convex, not continuous and minimizing it is NP-hard.
So we need to relax it, which basically means we want to find a convex upper bound for this loss.
This is not done on the actual prediction, but on the inner product $w^T x$, which is also called the decision function.
So this graph here has the inner product on the x axis, and shows what the loss would be for class 1.
The 0-1 loss is zero if the decision function is positive, and one if it's negative.
Because a positive decision function means a 