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I'm aware of When are Machine Learning questions on-topic?, but I think maybe there's room for discussion regarding the tag.

I've made a search on the ML tag (because I do not have access to the SE Query... thing) and figured that there are actually 66% of unanswered questions tagged with . (Maybe my search wasn't good though)

I think that, due to the nature of Machine Learning, it is very hard to make a valuable review regarding performance/accuracy without having :

  1. Access to the data
  2. Time to fiddle with it
  3. Experience in the Machine Learning field

That is without considering the fact that hyperparameters, which are often then key to great performances and can greatly affect speed, are pretty much chosen by trying different values.

I think that machine learning related questions will be more and more frequent and I'm wondering if they are really fitting here or if they should be posted to one of : ArtificialIntelligenceSE, DataScienceSE, CrossValidatedSE, etc(SE).

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    \$\begingroup\$ Sidenote: The "SE Query... thing" can be found at data.stackexchange.com \$\endgroup\$ – Vogel612 Feb 8 at 14:06
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    \$\begingroup\$ @Vogel612 well thanks! I thought it was reserved for the 10k+ users. I've validated my numbers \$\endgroup\$ – IEatBagels Feb 8 at 14:10
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    \$\begingroup\$ "I think that, due to the nature of Machine Learning, it is very hard to make a valuable review" absolutely. So if other sites can handle such questions better, we should ask them what they can do with it. \$\endgroup\$ – Mast Feb 8 at 14:23
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    \$\begingroup\$ Just because it's hard to answer the questions here doesn't mean that they are a bad fit for the site itself. \$\endgroup\$ – Simon Forsberg Feb 8 at 15:54
  • \$\begingroup\$ @SimonForsberg No, but they may be a better fit elsewhere. If they are, everybody would win by pointing them into the right direction. \$\endgroup\$ – Mast Feb 8 at 18:34
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    \$\begingroup\$ @Mast As long as you don't vote to close them with that reason, that's fine. Just be sure you understand the scope of the target site before you redirect people there. \$\endgroup\$ – Simon Forsberg Feb 8 at 22:26
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    \$\begingroup\$ @SimonForsberg Of-course, I wasn't suggesting that. Do we have regulars among us that happen to know a decent bit about the scope of the sites mentioned? They may be able to shed some light in the answers. \$\endgroup\$ – Mast Feb 9 at 12:41
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    \$\begingroup\$ But should we have some of them as a target for closing? For example the question codereview.stackexchange.com/questions/213806/… would probably be on-topic at Data Science. \$\endgroup\$ – Graipher yesterday
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I think it depends entirely on what is being reviewed.

Reviewing the choices made for fitting the model (choice of parameters / transformations / type of model) are less about the code and more about machine learning as a whole. I think these kinds of questions would be better suited on the suggested alternatives, because it's not as much reviewing the code, but the idea behind it.

On the other hand, working with large volumes of data means that writing good code can make a big difference. Looping over the data unnecessarily, huge joins filling up RAM, those are things that can be reviewed by looking at the code itself.

Languages like Python make it really easy to start working with ML without having much programming experience. That seems to me like a perfect oppurtunity for a review :)

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