AI Series: A Classification Problem

In a previous post on Machine Lending I mentioned that I'd be taking the free Machine Learning course offered by a Stanford professor. The first lectures are now available online and I continue to think about how one would write a program to determine which loans to invest in and which to avoid.

From the lecture:

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

If my understanding is correct, Machine Lending software would have:

• Task T = Advising the user whether or not to invest in a listing
• Experience E = The backlog of published data about listings, their Payoff rates, etc.
• Performance Measure P = The total return from investing in a loan (more detail in my Performance Measure post)

It would fall into the category of Supervised Learning since there is a "correct" answer on whether or not one should have invested in a listing (as measured by Performance Measure P.) And it would fall into the "Classification Problem" subset of supervised learning problems.

(Now, you might be able to change this into a regression problem if you changed the question from "Should I invest in this listing or shouldn't I?" to "How much should I invest in this listing?" But that's a topic for another post.)