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stat.ML2025
Interpretable Model-Aware Counterfactual Explanations for Random Forest
Joshua S. Harvey, Guanchao Feng, Sai Anusha Meesala +2
Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to pro…
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…