perpetual
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
What is perpetual?
Perpetual is a high-performance gradient boosting machine that eliminates the need for hyperparameter optimization by using a simple `budget` parameter to control model accuracy. It delivers optimal performance in a single run and includes advanced features like causal ML, drift monitoring, and continual learning. Built with a Rust core, it provides zero-copy bindings for Python and R, making it fast and production-ready.
Data scientists and machine learning engineers who need efficient, high-performance gradient boosting without the overhead of hyperparameter tuning, especially those working in Python or R environments.
Developers choose Perpetual for its hyperparameter-free approach, which saves time and computational resources while delivering competitive accuracy. Its built-in advanced features like causal ML, drift monitoring, and continual learning provide out-of-the-box capabilities that are often missing in traditional GBMs.
Overview
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
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