Work out concept drift detection instantly with clear inputs, formula shown and shareable results.
The Drift Detection Method treats the error rate as a Bernoulli process and derives control limits from its standard error, sqrt(p(1-p)/n). Crossing two standard errors raises a warning and three signals concept drift — the relationship between inputs and target has genuinely changed. Because the limits shrink as the window grows, larger windows detect smaller shifts but respond more slowly.
DDM control limits
sigma = sqrt(p(1-p)/n); warning = p + 2 sigma; drift = p + 3 sigma
Data drift is a change in the input distribution; concept drift is a change in the mapping from inputs to the target. Only concept drift necessarily degrades accuracy.
Collect recent labelled data, evaluate the current model on it, then retrain on a window that emphasises the new regime. Alerting without a retraining path just produces ignored pages.