Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R

PKBoost: Adaptive GBDT for Concept Drift, Built from scratch in Rust, PKBoost manages changing data distributions in fraud detection with a fraud rate of 0.2%. It shows less than 2% degradation under drift. In comparison, XGBoost experiences a 31.8% drop and LightGBM a 42.5% drop

A collection of boosting algorithms written in Rust 🦀

Gradient Boosting Ensemble Machine Learning Technique to Combine a Collection of Weak Models into a Single, More Accurate & Efficient Predictive Model

Machine learning from the ground up in Rust

Gradient Boosting Library, written in Rust

RFGBoost: Gradient Boosting with Asynchronous Random Forests