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Machine learning models are omnipresent for predictions on big data. Onechallenge of deployed models is the change of the data over time, a phenomenoncalled concept drift. If not handled correctly, a concept drift can lead tosignificant mispredictions. We explore a novel approach for concept drifthandling, which depicts a strategy to switch between the application of simpleand complex machine learning models for regression tasks. We assume that theapproach plays out the individual strengths of each model, switching to thesimpler model if a drift occurs and switching back to the complex model fortypical situations. We instantiate the approach on a real-world data set oftaxi demand in New York City, which is prone to multiple drifts, e.g. theweather phenomena of blizzards, resulting in a sudden decrease of taxi demand.We are able to show that our suggested approach outperforms all regardedbaselines significantly.

Handling Concept Drifts in Regression Problems -- the Error Intersection Approach