Random forest and gradient boosting algorithms, including XGBoost, have become dependable tools for analyzing biological data because they tolerate messy, mixed-type inputs without extensive ...

Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.