ML 59: An Anomaly Score Ranks; the Contamination Setting Only Cuts
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. An isolation forest on simulated data with 0.5%, 2% or 10% planted anomalies ranked them with AUCs of 0.99 or more, and flagged exactly the share set by its contamination parameter; the ‘auto’ setting flagged 10....