In pricing studies for mature technological markets, researchers face a bulk of models and variants. They often use brand-price conjoints combined with individual consideration sets. With 200+ variants, however many concepts never enter any consideration set, leading to very sparse data. I am comparing HB, Xgboost and individual logistic regressions in terms of speed and validity, discussing where Xgboost can have an edge on other methods and how to apply it in practice.
