perpetual-ml / perpetual

What it does

• Hyperparameter-Free Learning. Achieves optimal accuracy in a single run via a simple budget parameter, eliminating the need for time-consuming hyperparameter optimization.
• High-Performance Rust Core. Blazing-fast training and inference with a native Rust core, zero-copy support for Polars/Arrow data, and robust Python & R bindings.
• Comprehensive Objectives. Fully supports Classification (Binary & Multi-class), Regression, and Ranking tasks.
• Advanced Tree Features. Natively handles categorical variables, learnable missing value splits, monotonic constraints, and feature interaction constraints.
• Built-in Causal ML. Out-of-the-box support for causal machine learning to estimate treatment effects.