Thanks for the kind words. I have been working with gradient boosting machines (GBM) a lot lately but haven’t yet written an in-depth explanation article!

The two sources I relied on for an overview of the GBM are:

• https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d
• http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/

An in-depth examination of the GBM that I found very helpful is this paper:

• “Tree Boosting with XGBoost: Why Does XGBoost Win “Every” Machine Learning Competition”

I use GBM in this article although I don’t explain how it works. If you understand a decision tree, you’re already well on your way to comprehending the GBM!