2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
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…
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
Joshua Rosaler, Dhruv Desai, Bhaskarjit Sarmah +4
We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF…