I have a lot (thousands are possible) of automatically-generated ordinal features that i'd like to exploit , to differentiate between two classes. I'm looking for some measure that will select the most-likely-to-be-useful ones . For instance I could look at the frequency of occurrence of each feature for one class vs. the other class, and go for those features that have the greatest delta .
If the features are ngrams from given names, and the classes are latin-american vs. american names , the frequency histograms look like this : latin ngrams, english ngrams
Is there some better way to go about this than looking for large frequency deltas? The scikit-learn classifiers allow you to check for feature usefulness post-facto, I was looking to do this pre-classifier.