perpetual-ml/perpetual
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
Perpetual helps data scientists and machine learning engineers quickly build highly accurate predictive models without the hassle of complex tuning. You provide your raw data, and Perpetual automatically generates an optimized model that can classify, regress, or rank outcomes. This is ideal for those who need reliable predictions from large datasets and want to avoid extensive hyperparameter optimization.
664 stars and 322 monthly downloads. Actively maintained with 4 commits in the last 30 days.
Use this if you need to build accurate predictive models for classification, regression, or ranking tasks and want to achieve optimal results quickly without manually tuning countless parameters.
Not ideal if you require absolute transparency into every single hyperparameter setting or if you are working with extremely small, simple datasets where manual tuning is trivial.
Stars
664
Forks
37
Language
Rust
License
Apache-2.0
Category
Last pushed
Mar 06, 2026
Monthly downloads
322
Commits (30d)
4
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