aximize the probability of the training set under this model.
This approach is known as maximum likelihood. Basically you want to find w and b such that they assign maximum
probability to the labels observed in the training data. You can rearrange that a bit and end up with this equation here,
which contains the log-loss as seen on the last slide.

The prediction is the class with the higher probability. In the binary case, that's the same as asking whether
the probability of class 1 is bigger or smaller th