2 papers
stat.ML2025
Explainable Unsupervised Anomaly Detection with Random Forest
Joshua S. Harvey, Joshua Rosaler, Mingshu Li +2
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real d…
stat.ML2024
Quantile Regression using Random Forest Proximities
Mingshu Li, Bhaskarjit Sarmah, Dhruv Desai +4
Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determini…