[MUSIC] As we've seen, overfitting decision trees
is a fundamental problem that must be addressed. And we've looked at a couple different
ways to address this problem. We looked at early stopping. So as we go down the tree, we're going to stop early to avoid getting
to a very complex tree and overfitting. And then we talked about pruning, where
we take a massive tree and then we chop, chop, chop, chop and simplify it. Pruning is very important in
practical settings, and so we spent quite a of bit of time going
through the details of that but it was optional section in the lecture. But I just want to make sure, we're all aware that if you
see an algorithm out there for decision trees that does pruning, that's
a kind of idea of what's being executed. So overfitting decision trees
is a fundamental topic, but one that now we have
a handle on how to address. And let me close by thanking my colleague,
Krishna Sridhar, who has been really fundamental
at creating these slides and being able to make this
lecture as great as possible. [MUSIC]