Tuning xgboost in R: Part II

In this previous post I discussed some of the parameters we have to tune to estimate a boosting model using the xgboost package. In this post I will discuss the two parameters that were left out in part I, which are the gamma and the min_child_weight. These two parameters are much less obvious to understand but they can significantly change the results. Unfortunately, the best way to set them changes from dataset to dataset and we have to test a few values to select the best model. Note that there are many other parameters in the xgboost package.