142

I'm currently working on many machine learning and computational algorithms, such as Singular Value Decomposition, Support Vector Machines, and others.

I'd like to ask questions about these topics, but I don't know which Stack Exchange website to use to post my questions.

How do I tell? What criteria do I use?

Improve this question
1

8 Answers 8

Reset to default
70

I've ended up here after asking this on MetaStackOverflow, as the question of where to send "pure Machine Learning" offtopics remains unclear (to me). My initial guess was that the place to put this stuff is CrossValidated (stats), but here is my analysis on the matter:

There are several possibilities for asking ML questions right now (ordered by site traffic):

  1. CrossValidated: enter image description here

    a) It includes machine learning in its main topics:

    Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

    b) machine-learning is the 4th most popular tag in the site (3359 tagged questions).

  2. ComputerScience (beta) enter image description here

    a) It mentions "Machine Learning" in its help page, but not in its main page:

    Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science.

    b) machine-learning is the 22th most popular tag. The site seems to be more about algorithms.

  3. Computational Science is not related to Machine Learning according to its Help page.

  4. Theoretical Computer Science enter image description here

    a) According to its Help Center,

    TCS covers a wide variety of topics including algorithms, data structures, computational complexity, parallel and distributed computation, probabilistic computation, quantum computation, automata theory, information theory, cryptography, program semantics and verification, machine learning, computational biology, computational economics, computational geometry, and computational number theory and algebra.

    Work in this field is often distinguished by its emphasis on mathematical technique and rigor.

    On the other hand, it also says:

    For questions other than research-level questions in TCS, you may want to consider the following places to ask:

    General Artificial Intelligence — Meta Optimize

    Statistics and Data Mining — Cross Validated ...

    What I understand of all this is (and the word "Theoretical" seems like a hint) is that TCS is the right place for theoretical machine learning questions, leaving practical questions on the side.

    b) machine-learning is the 24th most popular tag in the site.

  5. Data Science enter image description here

    This claims to be a place for "machine learning professionals", and machine-learning is the most popular tag! (263 questions).

Given all this information, I see there are 2 different options:

a) If the question is practical ("How do I split my 5000 sample training set to improve my SVM performance"), I see two possibilities: CrossValidated and Data Science. Since the final goal is to get help, I'd rather go for CrossValidated as it has a considerable traffic and machine learning seems like a relevant topic in there.

b) If the question is about machine learning theory (for instance, understanding how a neural network or SVM works), there are two natural options: Computer Science or Theoretical Computer Science. If the question is research-level and theoretical (e.g., questions about PAC learning, provable performance guarantees), ask on Theoretical Computer Science. If the question is not research-level, ask on Computer Science.

Improve this answer
8
  • 1
    I would add a final option (c) reminding that programming-related machine-learning questions do belong on Stack Overflow. After all we already have more that 6300 ML questions on SO... Commented Apr 22, 2015 at 12:47
  • That's true, but I'm talking about **pure**(not programming-related) ML questions... Commented Apr 22, 2015 at 15:33
  • 1
    There's a problem with your bottom-line advice a). TCS is only for research-level questions. Therefore, the examples you give are not suitable for TCS. I've edited your final recommendation to recommend either TCS or CS for theory questions, depending on whether the question is research-level. Commented Apr 22, 2015 at 20:06
  • 2
    Other feedback: It sounds like you've concluded that Stats.SE is not a great place for questions about machine learning theory. Do you want to elaborate on the basis for that conclusion? In my experience Stats.SE often seems open to theoretical/conceptual questions as well (though they might come from a slightly different perspective than computer scientists). Commented Apr 22, 2015 at 20:48
  • 8
    The fact that this question took so long to answer (and that I'm still confused after reading it) tells me that something is wrong... Commented Mar 5, 2018 at 11:39
  • 1
    Wouldn't it be opportune to open a dedicated 'machine learning' SE website in order to centralize question about frameworks, theoretical and practical aspects? I'm freely asking... Commented Apr 15, 2019 at 9:52
  • CrossValidated just took my question down. In fact, stackoverflow.com even banned me for posting ML questions. They think my minimal codes were not minimal enough... Commented Dec 1, 2021 at 21:53
  • It would be good to update the answer to also include ai.stackexchange.com Commented Jun 8 at 15:01
22

Should I use Computer Science one? Should I use Theoretical Computer Science one? Should I use Computational Science one? Should I use Statistical Analysis one?

Machine learning should be on-topic for either Computer Science or Cross Validated. If your questions are at least graduate study level and related to computer science theory (as explained in their scope), then they should be acceptable at Theoretical Computer Science as well. Do any of those sites have questions similar to the ones you want to ask that are currently getting answered? If so, then that's the one I'd pick.

Improve this answer
0
12

Questions about use and selection of machine learning algorithms, or about machine learning generally, are appropriate for DataScience.SE, where machine-learning has been the most popular tag in the first month of private and public beta.

However, questions about implementing or design of algorithms are probably more appropriate for one of the computer science sites that @BilltheLizard mentioned. See also: Are questions about algorithm implementation on-topic?

Improve this answer
4

Research level questions should go to Theoretical CS, anything lower should go to CS. If it's a question about a statistical method, either within the model or analyzing output, and it doesn't require any CS knowledge, I'd think Cross-validated (stats) would be best.

There have been a couple area 51 proposals for AI and ML that have actually made it to beta, but both were shut down after private, I believe due to a lack of experts and expert-level questions.

Improve this answer
4

One way to select the site is to think what main tag you will use and then check popularity of that tag in each of the sites.

For example, if you want to ask about feature engineering, your results will be:

  1. Computer Science

'feature' tags in computer science

  1. Theoretical Computer Science

'feature' tags in theoretical computer science

  1. Computational Science

'feature' tags in computational science

  1. CrossValidated

'feature' tags in cross validated

  1. Data Science

'feature' tags in data science

  1. StackOverflow

'feature' tags in stackoverflow

Based on that, I'd say that feature engineering questions should go to CrossValidated or Data Science.

Improve this answer
4

In additional to what others have written, I would add:

Keep in mind that each of these sites have a slightly different community and target audience and have some topic overlap.

E.g. if you want a statistician's perspective on a problem, CrossValidated would be the choice, if it is a undergrad ML course topic, CS would be the choice, if you an answer from the perspective of Machine Learning Theory researchers, TCS would be the choice, if you want an answer from Data Science practitioners, Data Science would be the choice, ...

Consider who might be the best person to answer your question and then check if the question would be on-topic on their site. Try to explain what kind of answer your are looking for and why.

Also if you don't get the answer you were looking for on one site, people are typically nice enough to point you to a more appropriate site, and many sites allow cross-posting as long as it is not simultaneous and you link to the previous copies and explain why the answers there didn't satisfy what you were looking for.

Improve this answer
3

Note that there are also several Area51 sites/proposals about Artificial Intelligence or Machine Learning. For example here.

Improve this answer
1
  • Artificial Intelligence SE has been live for some time now. Commented Nov 23, 2020 at 23:36
2

DataScience and Artificial Intelligence will do.

Improve this answer

You must log in to answer this question.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.