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Build an algorithm to predict friendships, then actually use it to meet people

joingrouper.com

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Re: Build an algorithm to predict friendships, then actually use it to meet people

#4
This is interesting, but given your parameters (predict the most friendships), all you're technically asking for is recall. I'll write an algorithm that has 100% recall: predict that all people become friends with each other.

If this is really a competition (and not just "Here, have fun with our dataset!"), you need to define the rules a little bit more clearly. How are you weighing recall vs. precision? Or are you just looking at % correct labels, where the only two labels possible are "FRIENDS" and "NOT FRIENDS"?

Re: Build an algorithm to predict friendships, then actually use it to meet people

#7

This would be a little more fun if there was a cash prize. No offense meant, groupers look cool, but you'd probably get some more participation that way.

But then they could just hire a M.S. in Computer Science?

Re: Build an algorithm to predict friendships, then actually use it to meet people

#9
This is a cool challenge but the prize is definitely lacking. I think anyone capable of writing an algorithm of the caliber you're looking for isn't likely to participate. I could be wrong but I think you're going to have to pony up some serious cash to get developers taking this seriously. Or you could go the more standard route and just hire someone to do the job.

Re: Build an algorithm to predict friendships, then actually use it to meet people

#10

This is interesting, but given your parameters (predict the most friendships), all you're technically asking for is recall. I'll write an algorithm that has 100% recall: predict that all people become friends with each other. If this is really a competition (and not just "Here, have fun with our dataset!"), you need to define the rules a little bit more clearly. How are you weighing recall vs. precision? Or are you j…

Sorry this was unclear. We meant "correctly predict the most friendships"

You get 1 point for each friendship that you correctly predict did or did not occur. In the test data set ~50% of pairs became friends, so predicting "everyone became friends" would get 250 points, whereas a perfect algorithm would get 500 points.

I'm updating the README now to make our scoring system more clear.

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