Performance Metrics for Machine Learning

Thu, 09 Jul 2026 21:02:56 +1000

a model is only as good as the number you judge it by, and most of the classic modelling disasters are really metric disasters — a fraud detector with \(99.9\%\) accuracy that never flags anything, a medical test tuned to a roc curve nobody deployed at the published threshold. 𐃏 this page is the field guide: what each metric measures, what it silently assumes, and which one to reach for when the classes are lopsided, the probabilities matter, or the target is continuous.

Class-Imbalance on Aayush Bajaj's Augmenting Infrastructure

Performance Metrics for Machine Learning