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Oct 12, 2020 at 18:44 history edited klumbard CC BY-SA 4.0
cleaned things up a bit
Oct 12, 2020 at 18:22 comment added Ben Reiniger fwiw, There were some discussions for model-based recursive partitioning in sklearn: github.com/scikit-learn/scikit-learn/issues/11647
Oct 12, 2020 at 16:42 history edited klumbard CC BY-SA 4.0
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Oct 12, 2020 at 16:30 comment added Sycorax @HansHupe sklearn is convenient, but limiting yourself to the models that it implements will not give you very deep coverage of the kinds of problems you might need to solve. Indeed, you've discovered one such blindspot in the case of this problem.
Oct 12, 2020 at 16:02 comment added HansHupe Sorry for complaining, but it looks like that no algorithm of the widely used ML toolbox sklearn in Python is able to learn that rule in a satisfying way (maybe MLPRegressor could but it would be overshooting I think and needs a lot of tuning). For me this structure of problem is a very practical challenge.
Oct 12, 2020 at 15:54 comment added Sycorax @HansHupe This is kind of like complaining that a screwdriver doesn't do a very good job of driving nails. There are many types of machine learning models because there are many types of problems, and a model which does well against one problem may not do well against another type of problem. For more information, you may be interested in reading about the so-called "No Free Lunch Theorem." en.wikipedia.org/wiki/No_free_lunch_theorem
Oct 12, 2020 at 15:46 history edited klumbard CC BY-SA 4.0
edited body
Oct 12, 2020 at 15:41 comment added HansHupe Thx. for sharing this I got the idea. I think model trees (M5) are a nice approach, unfortunately poorly supported in Python (sklearn). But honestly I am surprised that such simple interaction rules are obviously not an easy thing for standard ML methods.
Oct 12, 2020 at 15:37 vote accept HansHupe
Oct 12, 2020 at 15:35 history answered klumbard CC BY-SA 4.0