

Decision tree learner will predict whether you prefer cats or dogs - sllrpr
http://cord-sa.appspot.com/

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sllrpr
I'm happy to answer any questions - this is based on the QuickDT library (see
<https://github.com/sanity/quickdt>).

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bockris
Followup: I see now that 'city' is in and 'region' is out.

Did you do that or did it figure it out on it's own.

Is there anyway to see the full decision tree without re-voting? I've
accidentily refreshed a couple of times so my extra votes are skewing the tree
but I'm interested in seeing how the tree evolves.

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sllrpr
It figures it out on its own.

You can see the entire tree at <http://cord-sa.appspot.com/dumpDT>

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bockris
Nice!

How big is the pool of variables it can choose from?

Any insight on why it choose 'region' and 'browserHeight' first?

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sllrpr
Ugh, I thought I'd answered this but looks like HN lost my response :-(

> How big is the pool of variables it can choose from?

Currently it is just: screenWidth/Height, browserWidth/Height, browser,
browserVersion, os, referrer, city, region, country.

It picks the variables based on how well they partition the datasets into
diverse sub-groups. This is the code that does that job:

[https://github.com/sanity/quickdt/blob/master/src/main/java/...](https://github.com/sanity/quickdt/blob/master/src/main/java/quickdt/scorers/Scorer1.java)

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byoung2
100% certain I prefer dogs based on browser height and region? Not likely.

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sllrpr
Give it a chance, it only has a small dataset to work with so-far.

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mbetter
Thomas Bayes is now spinning in his grave.

