leaves
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
What is leaves?
Leaves is a pure Go implementation of the prediction component for Gradient Boosting Regression Trees (GBRT) models. It allows developers to load and run models trained in frameworks like LightGBM, XGBoost, and scikit-learn directly within Go applications, eliminating the need for C bindings or external dependencies. The library focuses on efficient, high-performance inference for serving machine learning models in production Go environments.
Go developers and engineers who need to integrate pre-trained GBRT models into Go-based services, applications, or pipelines for real-time predictions, such as in microservices, APIs, or data processing systems.
Leaves provides a native Go solution with performance rivaling original C APIs, reduces deployment complexity by removing external dependencies, and offers a clean, idiomatic Go API for seamless model inference integration.
Overview
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
Use Cases
Best For
- Serving LightGBM or XGBoost models in Go microservices
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