Concept drift is the shift in the statistical properties of the target/dependent variable(s), i.e a drift in the actuals. Specifically, the drift of current ground truths from previous ground truths from a prior time window or training dataset.
Example:
Your churn model could start to drift if a global pandemic makes it more difficult for customers to pay for service. Therefore your model may drastically underestimate churn.
