Let's use the decision tree that you trained in the first exercise. The tree predicts whether a loan applicant will default on their loan (or not).

Assume we have a loan applicant who:

- is applying for a 20-month loan
- is requesting a loan amount that is 2% of their income
- is 25 years old

After following the correct path down the tree for this individual's set of data, you will end up in a "Yes" or "No" bucket (in tree terminology, we'd call this a "leaf") which represents the predicted class. Ending up in a "Yes" leaf means that the model predicts that this individual *will default* on their loan, where as a "No" prediction means that they *will not default* on their loan.

Starting with the top node of the tree, you must evaluate a query about a particular attribute of your data point (e.g. is `months_loan_duration < 44`

?). If the answer is yes, then you go to the left at the split; if the answer is no, then you will go right. At the next node you repeat the process until you end up in a leaf node, at which point you'll have a predicted class for your data point.

According to the model, will this person default on their loan?

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