Concept Drift
The relationship between input and outcome changes while the inputs look the same — the drift you cannot detect without labels, and therefore the one that reaches production undetected.
Reviewed July 16, 2026Stable
When not to use it
- As a diagnosis before ruling out a pipeline change, which produces identical symptoms and is far quicker to fix.
- As a synonym for data drift. They need different detection and different responses.
Reach for something else instead
- Data drift monitoring where labels are unavailable, accepting that it cannot see this failure.
- Scheduled retraining where verification latency makes detection impossible.
- Online learning where labels arrive quickly and noise is controlled.
Sources & further reading
- Gama et al. (2014), A Survey on Concept Drift Adaptation — the standard taxonomy of drift types and detection methods.
- Bifet & Gavaldà (2007), Learning from Time-Changing Data with Adaptive Windowing — the ADWIN detector.
- Lu et al. (2019), Learning under Concept Drift: A Review — a more recent synthesis including the verification-latency problem.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Monitoring inputs only, which cannot detect it by construction.
- Assuming stable accuracy means stable relationships, when the test set was frozen before the drift began.
- Retraining on recent data without checking whether the recent labels are themselves affected.
At a glance
FieldApplied AI
ChangesP(Y|X) not P(X)
Detectable fromlabels only
Warning timeusually none
Worst caseverification latency of months
DifficultyPractical
Where this sits
A destination. 5 concepts lead here, and nothing in the corpus depends on it.
5Levelsteps in
5Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstData Drift
Computed from the prerequisite graph, not assigned. How this works