Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

Ryuichi Kanoh

Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous sing…