Very low probability in naive Bayes classifier 1
Each column of binary (Y) is a feature. The Bernoulli naive Bayes classifier could identify the class (X) where the number of features (Y) was less than 17. The real data had more features than that. I found that another method could classify it accurately. That was:

Trainining:

(1) Count which features (Y) are in each class (X) in the training data

Testing:

(2) Give each row a score (Z) with a starting value of 0.5

(3) For each row:


If each feature (Y) is in the class (X) in the training data then add 1 to the score (Z).
If each feature (Y) is not in the class (X) in the training data then subtract 1 from the score (Z).
If the class (X) is not in the training data then don't do anything


The score (Z) was a good classifier for my data.