[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]