2 papers
stat.ME2025
Targeted tuning of random forests for quantile estimation and prediction intervals
Matthew Berkowitz, Rachel MacKay Altman, Thomas M. Loughin
We present a novel tuning procedure for random forests (RFs) that improves the accuracy of estimated quantiles and produces valid, relatively narrow prediction intervals. While RFs…
stat.ML2024
Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests
Nikola Surjanovic, Andrew Henrey, Thomas M. Loughin
We demonstrate that adaptively controlling the size of individual regression trees in a random forest can improve predictive performance, contrary to the conventional wisdom that t…