Supervised learning using Boltzmann Bayes model inference,
which extends naive Bayes model to include interactions. Enables
classification of data into multiple response groups based on a large
number of discrete predictors that can take factor values of
heterogeneous levels. Either pseudo-likelihood or mean field
inference can be used with L2 regularization, cross-validation, and
prediction on new data.
Woo et al. (2016)