Gradient-boosted decision trees are a commonly used machine learning algorithm that performs well on real-world tabular datasets. There are many libraries available for training them, most commonly LightGBM and XGBoost. Sadly few of the popular libraries are optimized for fast prediction & deployment. As a remedy, I spent the last few months building lleaves, an open-source decision tree compiler and Python package.
