How to identify statistically distinct features of different sets? 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.

Thanks!
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.