Model Drift
Model Drift Model drift is the general phenomenon where a deployed model’s predictive performance degrades over time, even though nothing about the model itself has changed. The model is the same; the world it operates in isn’t. Taxonomy Drift is usually classified by what’s shifting in the underlying probability distributions. Data drift (covariate shift) The distribution of input features P(X) changes, but the relationship P(Y|X) stays the same. A fraud detection model starts seeing a higher fraction of mobile-wallet payments — inputs look different, but the rules for “is this fraud” haven’t changed. ...