MPBART - Multinomial Probit Bayesian Additive Regression Trees
This provides a flexible tool for discrete choice and multiclass classification problems, though it is incremental as an extension of existing BART methods.
The authors tackled the problem of multinomial choice modeling by extending Bayesian Additive Regression Trees (BART) to a multinomial probit framework, resulting in MPBART, which demonstrated very good predictive performance in simulations and real data examples compared to other methods.
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and multiclass classification methods. To implement MPBART, we have developed an R package mpbart available freely from CRAN repositories.