[MUSIC] We've discussed a little bit of
this loan application domain and showed you a little example
of the decision tree. Let's dive in and explore the decision
tree a little bit further. So, I start for my loan application and
I could go down one branch or one path of this decision tree and
say If this application somebody would file credit that's fair and
is a short-term loan, let's say three year loan, then this
is considered a risky application. If I take another path of
the decision tree, let's say, I'll look at somebody whose credit
is poor but the income is high but it's a short term loan again,
I might consider it a risky application. In general I'm going to
be given an imput xi. In this case it has assignments to each
one of those input features that say credit poor, income high, and
term five years longer term loan. And then I traverse the path down the tree
to make a prediction which in this case y hat i would be safe. So this is the model predicts
to be a safe loan to make. So just in generally, the task that we're
looking at is given some input, xi, I'm going to traverse the decision tree down
the particular branches of that input, which in the example that we just did
was a traversal that looks like this. And then I'm going to make
a prediction y-hat i. Which might be that this loan is safe and
so our goal is to learn that tree T(x) that let's us make
predictions about our data. [MUSIC]