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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.
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