classification: when to use 0 for negative label and when to use -1 In classification problem(e.g. in the click-through-rate prediction problem which is to predict whether a user will click an item or not), we usually use +1 for positive label and 0 for negative label. This is naturally good. But is it a necessity? what's the difference between using -1 for negative label, or even more generally, can we use +100 for positive label and -100 for negative label?

As also stated in the comments, it is not a necessity. It's a convenience when formulating your problem. It's also useful to think about how you're going to penalize misclassifications. 0-1 labelling naturally allows you to use cross entropy, which is typically a good loss function choice for the problem, however for $\{-1,1\}$, you need to play with the result a bit or use a different loss. However, intuitively, this is not better than $[0,1]$ range. For $\{-100,100\}$, it seems like it's equivalent to $\{-1,1\}$ range, however, a couple of mistakes can make your learning much harder due to numerical issues.
