https://lapark.github.io/tags/multi-label-classification/Recent content in Multi-Label Classification on Laurence A. F. ParkHugoen-usThis page, its contents and style, are the responsibility of the author and do not necessarily represent the views, policies or opinions of Western Sydney University. The header image Memorial to Folly is unofficial Fan Content permitted under the Fan Content Policy. Not approved/endorsed by Wizards. Portions of the materials used are property of Wizards of the Coast. ©Wizards of the Coast LLC. All other content &copy; Laurence ParkWed, 01 Jan 2025 00:00:00 +0000https://lapark.github.io/news/estimating-multilabel-expected-accuracy/Wed, 01 Jan 2025 00:00:00 +0000https://lapark.github.io/news/estimating-multilabel-expected-accuracy/<p>When a multi-label classifier makes a prediction — say, flagging a patient record for Diabetes, Hypertension, <em>and</em> COVID-19 — how confident should you be? This question is harder than it looks. In a single-label setting, the probability score attached to a prediction is a straightforward measure of confidence. In the multi-label world, it gets complicated fast.</p> <p>A new paper by <strong>Laurence A. F. Park</strong> (Western Sydney University) and <strong>Jesse Read</strong> (École Polytechnique) takes a rigorous look at this problem, testing seven candidate functions for estimating expected accuracy from a multi-label probability distribution — and finding clear winners depending on how accuracy is measured.</p>