Synced Tree Boosting With XGBoost – Why Does XGBoost Win "Every" Machine Learning Competition?

Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting method of choice. But a starting from 2015, a first to try, always winning algorithm surged to the surface: XGBoost. This algorithm re-implements the tree boosting and gained popularity by winning Kaggle and other data science competition. In the thesis Tree Boosting With XGBoost – Why Does XGBoost Win "Every" Machine Learning Competition, the author Didrik Nielsen from Norwegian University of Science and Technology is trying to: The paper introduce in first place the supervised learning task and discuss the model selection techniques.