Let's say I want to build a system that will automatically filter out resumes based on the information in them. A big problem that technology companies have is that we get tons and tons of resumes for our positions. We have to decide who we actually bring in for an interview, because it can be expensive to fly somebody out and actually take the time out of the day to conduct an interview. So what if there were a way to actually take historical data on who actually got hired and map that to things that are found on their resume?

We could construct a decision tree that will let us go through an individual resume and say, "OK, this person actually has a high likelihood of getting hired, or not". We can train a decision tree on that historical data and walk through that for future candidates. Wouldn't that be a wonderful thing to have?

So let's make some totally fabricated hiring data that we're going to use in this example:

In the preceding table, we have candidates that are just identified by numerical identifiers. I'm going to pick some attributes that I think might be interesting or helpful to predict whether or not they're a good hire or not. How many years of experience do they have? Are they currently employed? How many employers have they had previous to this one? What's their level of education? What degree do they have? Did they go to what we classify as a top-tier school? Did they do an internship while they were in college? We can take a look at this historical data, and the dependent variable here is Hired. Did this person actually get a job offer or not based on that information?

Now, obviously there's a lot of information that isn't in this model that might be very important, but the decision tree that we train from this data might actually be useful in doing an initial pass at weeding out some candidates. What we end up with might be a tree that looks like the following:

• So it just turns out that in my totally fabricated data, anyone that did an internship in college actually ended up getting a job offer. So my first decision point is "did this person do an internship or not?" If yes, go ahead and bring them in. In my experience, internships are actually a pretty good predictor of how good a person is. If they have the initiative to actually go out and do an internship, and actually learn something at that internship, that's a good sign.
• Do they currently have a job? Well, if they are currently employed, in my very small fake dataset it turned out that they are worth hiring, just because somebody else thought they were worth hiring too. Obviously it would be a little bit more of a nuanced decision in the real world.
• If they're not currently employed, do they have less than one prior employer? If yes, this person has never held a job and they never did an internship either. Probably not a good hire decision. Don't hire that person.
• But if they did have a previous employer, did they at least go to a top-tier school? If not, it's kind of iffy. If so, then yes, we should hire this person based on the data that we trained on.