So that's how you walk through the results of a decision tree. It's just like going through a flowchart, and it's kind of awesome that an algorithm can produce this for you. The algorithm itself is actually very simple. Let me explain how the algorithm works.

At each step of the decision tree flowchart, we find the attribute that we can partition our data on that minimizes the entropy of the data at the next step. So we have a resulting set of classifications: in this case hire or don't hire, and we want to choose the attribute decision at that step that will minimize the entropy at the next step.

At each step we want to make all of the remaining choices result in either as many no hires or as many hire decisions as possible. We want to make that data more and more uniform so as we work our way down the flowchart, and we ultimately end up with a set of candidates that are either all hires or all no hires so we can classify into yes/no decisions on a decision tree. So we just walk down the tree, minimize entropy at each step by choosing the right attribute to decide on, and we keep on going until we run out.

There's a fancy name for this algorithm. It's called ID3 (Iterative Dichotomiser 3). It is what's known as a greedy algorithm. So as it goes down the tree, it just picks the attribute that will minimize entropy at that point. Now that might not actually result in an optimal tree that minimizes the number of choices that you have to make, but it will result in a tree that works, given the data that you gave it.