[MUSIC] We've now learned
decision trees from data. Let's look at how we can
make predictions from it. So given the model that we've learned from
the data, we're going to predict y hat. As we discussed in the beginning of
the module, it's a pretty simple process of traversing down the tree in
order to make that prediction. Given that particular input xi, so going
back to that first example where Credit was poor, Income was high, and the Term
was 5 years, we go down the branch of the decision tree that is associated
with those particular inputs, and we make a prediction that y hat i is Safe. This would be explicit, even that prediction algorithm is
what's called a recursive algorithm. I'm trying to predict, for
a particular decision tree, I start with the top node and
my input set. And then I say,
if the current node is a leaf, return, predict whatever is on that leaf. If it's not a leaf, then pick the child
that agrees with the input and then recurse on that child. So return the prediction the child makes. And then that process is going to
unroll that path down the tree in order to make the prediction. [MUSIC]