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 and mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. Woo et al. (2016) <doi:10.1186/s12864-016-2871-3>.
