When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems

Feature collapse occurs when a machine learning model over-optimizes and begins to rely on only a small number of high-signal features, ignoring the rest of the input space. This process makes the model brittle and less adaptable to new data or edge cases, even while standard performance metrics appear healthy. This issue often goes undetected because typical MLOps pipelines do not monitor the evolution of feature importance over time. Proposed solutions include tracking attribution entropy with tools like SHAP, implementing feature dropout during training, and using multi-task learning to force the model to consider a wider variety of inputs.