Confidence Intervals for XGBoost - KDnuggets

Gradient Boosting methods are a very powerful tool for performing accurate predictions quickly, on large datasets, for complex variables that depend non linearly on a lot of features. The underlying mathematical principles are explained with code here. Moreover, it has been implemented in various ways: XGBoost, CatBoost, GradientBoostingRegressor, each having its own advantages, discussed here or here. Something these implementations all share is the ability to choose a given objective for training to minimize. And even more interesting is the fact that XGBoost and CatBoost offer easy support for a custom objective function.