Evaluating boosted decision trees for billions of users

What this post added

This post details significant advancements in optimizing gradient-boosted decision trees (GBDTs) for large-scale, real-time machine learning applications at Facebook. It introduces specific C++ implementation improvements, including compiled trees with annotations, range evaluation, and optimizations for common and categorical features, which resulted in up to a 5x performance improvement. These optimizations enable more frequent model updates and the use of larger, more precise models for ranking user experiences like notifications, News Feed, and recommendations, directly impacting the efficiency and effectiveness of AI-driven personalization for billions of users.