  With today's abundant streams of data, the only constant we can rely on is
change. For stream classification algorithms, it is necessary to adapt to
concept drift. This can be achieved by monitoring the model error, and
triggering counter measures as changes occur. In this paper, we propose a drift
detection mechanism that fits a beta distribution to the model error, and
treats abnormal behavior as drift. It works with any given model, leverages
prior knowledge about this model, and allows to set application-specific
confidence thresholds. Experiments confirm that it performs well, in particular
when drift occurs abruptly.
