  In this paper, we investigate how feature interactions can be identified to
be used as constraints in the gradient boosting tree models using XGBoost's
implementation. Our results show that accurate identification of these
constraints can help improve the performance of baseline XGBoost model
significantly. Further, the improvement in the model structure can also lead to
better interpretability.
