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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?
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):
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).
ComputerScience (beta)
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.
Computational Science is not related to Machine Learning according to its Help page.
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.
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.
(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...
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 theoretical (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.
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?
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.
Note that there are also several Area51 sites/proposals about Artificial Intelligence or Machine Learning. For example here.
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:
Based on that, I'd say that feature engineering questions should go to CrossValidated or Data Science.
DataScience and Artificial Intelligence will do.
