The following project was done to support research being done in the Astrophysics department at the George Washington University. The attached R notebook details an application of gradient boosted decision trees(XGBoost) as well as K-fold cross validation in the classification of X-ray objects.
I would like to thank and acknowledge Kalvir Dhuga and Eda Sonbas for the input and feedback.
I would also like to thank and acknowledge Tianqi Chen and Carlos Guestrin the creators of XGBoost (https://arxiv.org/abs/1603.02754) as well as the numerous contributors to the package (https://github.com/dmlc/xgboost).