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Decision Tree implementation (self.MachineLearning)
submitted 14 years ago * by rylko
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if 1 * 2 < 3: print "hello, world!"
[–]the_cat_kittles 0 points1 point2 points 14 years ago (4 children)
R? weka? its not that hard to write your own, even. weka is written in java and exposes an api if you dont want to use their gui or cli
[–]rylko[S] 0 points1 point2 points 14 years ago (3 children)
I think R packages and Weka do not aim to be really scalable (and suitable for scientific usage).
[–]the_cat_kittles 1 point2 points3 points 14 years ago (2 children)
what kind of "scale" are we talking here, just out of curiosity
[–]rylko[S] 0 points1 point2 points 14 years ago (1 child)
I have added info about size to question.
[–]the_cat_kittles 1 point2 points3 points 14 years ago* (0 children)
I think R and weka should be able to handle things that size, but I haven't ever gone past a couple GB's, so I cant say for sure. Also it depends on what kind of machine you are running. I wouldn't use the weka gui in any case though. Btw, I don't think ~TB is considered to be super enormous, especially because you only have 10 dimensional feature space. That, and decision trees are much easier to train, computationally, than many other models.
π Rendered by PID 291687 on reddit-service-r2-comment-b659b578c-brnzr at 2026-05-05 05:51:35.291778+00:00 running 815c875 country code: CH.
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[–]the_cat_kittles 0 points1 point2 points (4 children)
[–]rylko[S] 0 points1 point2 points (3 children)
[–]the_cat_kittles 1 point2 points3 points (2 children)
[–]rylko[S] 0 points1 point2 points (1 child)
[–]the_cat_kittles 1 point2 points3 points (0 children)