Training Data

Handle Label Noise in Supervised Learning

Supervised learning assumes that training examples come with useful target labels. Real datasets rarely satisfy that assumption perfectly. A support ticket may be assigned to the wrong queue, an image may receive the wrong class, or two annotators may interpret an ambiguous policy differently. These errors create label noise: the recorded target does not reliably represent the target the model is supposed to learn. Enough noise can teach a model contradictory patterns, distort evaluation, and make apparently difficult modeling problems into data-quality problems.