2010 AISTATS AISTATS 2010

Convexity of Proper Composite Binary Losses

Abstract

A composite loss assigns a penalty to a real-valued prediction by associating the prediction with a probability via a link function then applying a class probability estimation (CPE) loss. If the risk for a composite loss is always minimised by predicting the value associated with the true class probability the composite loss is proper. We provide a novel, explicit and complete characterisation of the convexity of any proper composite loss in terms of its link and its “weight function” associated with its proper CPE loss.

🚀 Conference Pioneer — AISTATS 2010
📈 Trend Setter — Loss Functions
🧭 Keyword Pioneer — class probability estimation
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
🐣 Hot Topic Early Bird — binary classification