Feature importance parameter in machine learning models like Naive Bayes
Since Naive Bayes assumes independence and outputs class probabilities most feature importance criteria are not a direct fit. The feature importance should be no different from the skewness of the feature distribution in the set: You could try to directly compare the probability of the features given the classes (implemented in sklearn for instance), the variability of those probabilities with respect to the classes should express the importance of those features.