Comment by jupiter90000 | Hacker News Reader

Comment by jupiter90000 | Hacker News Reader

Often this sort of material seems to be a collection of methods and understanding them, which is obviously important to being able to use them. However, I usually feel like the example problems are much cleaner and simpler than those I've encountered in business. I feel like there's this missing link between learning the methods and doing something that actually adds significant value for a business using machine learning. Perhaps it's just me or my field though.

I found that usually lots of work involved just transforming or examining data in relatively simple ways or using human expert decisions as to important threshholds for outliers. For example I could run an outlier algorithm on data and either the returned outliers were very obvious and could have been found using a manual query by knowing the business context, or it returned alot of false positive outliers that were useless for the business. Other times, we'd have a predictive model that was good for 95% of cases but would make our company look ridiculous on predictions for the other 5%, so couldn't use it in production-- and the nature of the data was such that we couldn't use the model for only certain value ranges.

Perhaps it was just the nature of our realm of business (telecom), and these approaches are more useful for others (advertising, stock trading, etc). Any experience with business fields where this stuff made a sizable impact for something they productionized in business they can share?

Depending on the business needs, returning outliers can be useful even if there are a bunch of false positives.

I'm not a machine learning guy, but when I was at Kongregate, we had a problem with credit card fraud on our virtual goods platform. It wasn't serious fraudsters, just dipshit teens with their parent's credit card.

I had labeled data: historical transactions, with chargebacks, which I fed into Weka. I included all kinds of stuff we knew about the user. A simple rule-based classifier could pick out risky transactions, with a lot of false positives.

I made a simple tool for our customer service team to review these risky transactions. They would decide whether to warn the user, temporarily block them from buying or temp ban them, or permanently ban them.

This worked pretty well for us. The risk factors were new players, players spending quickly, and users who were dicks - as measured by how often others had muted them in chat, how often they swore in chat, etc.

As an aside, saying "fuck" or "shit" in chat wasn't very predictive of fraud - often those terms aren't signs of an abusive user, since they might just be saying "fuck, I suck at this game". What was predictive was users who said "Gay", "Penis", or "Rape". People who use those terms on a game platform are largely dickheads. So the score for abusiveness became known as the "Gay, Penis, Rape Score" or "GPR" for short.

Very cool, thanks! I didn't realize that in certain contexts, many false positive outliers wouldn't necessarily be such a bad thing, especially when they could be further refined with human interaction.

I've had similar experience in insurance. Our predictive algorithms have been used sparingly and guides our strategy but we don't fully trust the actual data. That's how we leverage our analysis.

For us, small increments does give us sizeable impact. And we don't aim for predicting 100% of the cases either. We take what we get and see how we can use it.

In business, we don't care about accuracy. We care about improvement.

Thanks for your comment, this is exactly the type of information I was interested in.

Chiming in to say that I have the same exact experience :) I work in security, and we use these methods to detect anomalies or classify malicious content or URLs. A silly false positive is embarrassing, even if it happens once. Humans always augment our methods, or we have to set expectations to the customer that we are trading off accuracy for speed. Fast customer support usually helps against false positives too.

Yes, augmenting machine intelligence with human intuition is great because machines yet haven't got human intuition which we can't program.

While I agree that data munging is very important and very difficult, I disagree that it should be part of every course teaching any kind of data manipulation.

I took a course called data mining at university and it largely consisted of munging data.

Biased by that one course, I would expect anything called "data mining" to contain a lot of practice and theory about cleaning data and a machine learning course to focus on what to do with the cleaned data.

These are just introductory courses, teaching the theory.

Teaching best practices for applying these methods to particular fields is probably beyond the expertise of any one person. Perhaps there's an opportunity for professors or practitioners of each field here?

I would argue that if you the physics behind the problem, then even semi-empirical models easily beat machine learning. I have seen this consistently on my data-sets.

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