Gradient Boosting in TensorFlow vs XGBoost

Tensorflow 1.4 was released a few weeks ago with an implementation of Gradient Boosting, called TensorFlow Boosted Trees (TFBT). Unfortunately, the paper does not have any benchmarks, so I ran some against XGBoost. For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today, as it gracefully handles un-normalized or missing data, while being accurate and fast to train. The code to reproduce the results in this article is on GitHub.