Hey,
I’m the author of the EXGBoost library.
Just wanted to chime in here and see if you needed any help still on this.
Let me know!
acalejos
tegmentum
Hey Acalejos, awesome of you to reach out.
It would be great if you could give me some thoughts on the following (I’m assuming you’ve followed from the top 
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Given the numerical attributes like weight, volume, etc, and the item count, I’m normalizing all those values together, would you say it’s better I only normalize the numerical data, and keep the counts as they are?
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I’ve also been advised to try out a Random Forrest or SVM given that I only have 1800 rows, would you know where I could find implementations of those algorithms ? I’ve seen a library called Evision , but it seems a little outdated.
Thanks!
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acalejos
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The biggest thing I would worry about with any approach where you append a large dimension vector onto existing columns of data is just having too high dimensionality and then drowning out other features (saw it mentioned that it’d only be length 65 which should be ok, just something to be aware of). You could try both normalizing the count and not. It shouldn’t be too much trouble to experiment with that. Considering the count is ordinal already and depending on how you normalize it might not make any difference.
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Im not a ware of any random forest libraries, but there is an SVM implementation in Scholar, it just doesnt appear to be in an official release yet. But you can see it here: scholar/lib/scholar/linear/svm.ex at main · elixir-nx/scholar · GitHub
I’m pretty sure Evision is a vision library, so I’m not sure how that would help you. It also seems perfectly up to date to me, although I haven’t used it.
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