Why do TensorFlow models degrade when feature importance changes over time?

Feature drift occurs when the predictive power of features changes over time.

Even if input distributions remain stable, the importance of features can change due to external system or user behavior changes. TensorFlow models trained on historical feature importance become outdated, leading to degraded predictions. This is a subtle form of concept drift.