Climente-González, Oh et al. introduce an interpretable machine learning model that integrates plasma proteomics with clinical risk factors and employs an explainable boosting machine algorithm for risk prediction. Authors show that their model outperforms existing models in predicting risk for cardiovascular disease.

Using Hugging Face transformers

Exponentials are all you need

A Swiss Army Knife for Python data science

A model-agnostic framework for explaining predictions

Introduction A few things motivated this post.   Some recent discussions about the virtues of LightGBM vs XGBoost Posts on TabPFN by Jon...