Seeing that Naive Bayes uses probability to make a prediction, and treats features as being conditionally independent of each other, then it makes sense that the model can still make a prediction given that there are some features missing in the test data.
I know that it is common practice to impute missing data, but why do this when Naïve Bayes should be able to make a prediction given that there are some features missing?
Can this be implemented in sci-kit learn? I tried a test set with less features, and got a ValueError as the shapes are not aligned.
So theoretically this is possible, but is it possible in scikit learn?