learning
classification trees, you're learning regression trees,
which are trying to predict the probability, which is quite
different.

The question is, how they're different?

The same is true for f1. If I fit f1, exactly to y this
becomes 0. One reason is the gamma, which means I only go a
small step. But even if I set gamma equal to one, the point
is that I restricted my f1 to not completely fit the data.
I'm not trying to completely overfit the data, I'm trying to
use a simple model. So if I use a tree