I have two non-overlapping sets of items, with feature counts for each. What standard algorithms can I use to extract the most statistically distinct features of each set?

For example:

  • Items served at American restaurants (5 restaurants surveyed):
    • bread: 4
    • burgers: 2
    • cheese: 1
    • cronuts: 2
    • pasta: 2
  • Items served at Italian restaurants (10 restaurants surveyed):
    • bread: 7
    • pasta: 10
    • cheese: 8

I want to be able to know that cronuts and burgers are distinctly associated with American restaurant menus, and cheese and pasta are distinctly associated with Italian restaurant menus.



1 Answer 1


This looks like a standard machine learning problem. You could use any machine learning technique. You might start with Naive Bayes.

If you want to evaluate a single feature, you could use information gain or BIC.

For the combination of all features, you can use a machine learning algorithm. As I mentioned, I would suggest trying Naive Bayes first. If you need something more powerful, there are many other classifiers: random forests, SVM's, k-nearest neighbors. Read a textbook on machine learning to learn more about the subject.


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