I also had a very pleasant experience with Hunch. Last night I started on a new topic which I named “Am I a Maximizer or A Satisficer?” I was tired by then, so I didn’t add any questions or results. But when I checked back today, I discovered that someone else had continued my work. It was reassuring to see the crowd-sourcing at work.

But seriously, I agree that it would be interesting to know if they’re doing anything technically innovative with the decision trees.

Hunch’s top results essentially says, I’m ideally suited to work at a startup but it also suggest I be a veterinarian in Washington, D.C.

Now yes startup, I love animals but I’m no way near wanting to be a vet & Washington, D.C., are you kidding me! Now the 2 & 3 choice for place was Seattle & San Francisco which makes sense though I’m surprised New York didn’t show up. Jobs had entrepreneur & scientist as alternatives.

So at first blush it’s a fun little tool but not (as of yet at least) all that accurate.

I’m wondering what algorithm they are using for the underlying decision tree and if/how they are approaching new rule learning & boosting.

I’ll continue to toy around with it though. Maybe for food suggestions & the like it’ll be better.

I guess I’ll soon find out whether my manager, my alumni association, or my wife read my blog!

Thanks

I’d love one if you still have some.

Thanks,

Christopher