positive predition, means correct classification in the case of y=1.
A negative prediction means a wrong classification, which is penalized by the 0-1 loss with a loss of 1, i.e. one mistake.

The other losses we'll talk about are mostly the hinge loss and the log loss. You can see they are both upper bounds on the
0-1 loss but they are convex and continuous.
Both of these losses care not only that you make a correct prediction, but also "how correct" your prediction is, i.e.
how positive or negative your d