  Machine learning models are omnipresent for predictions on big data. One
challenge of deployed models is the change of the data over time, a phenomenon
called concept drift. If not handled correctly, a concept drift can lead to
significant mispredictions. We explore a novel approach for concept drift
handling, which depicts a strategy to switch between the application of simple
and complex machine learning models for regression tasks. We assume that the
approach plays out the individual strengths of each model, switching to the
simpler model if a drift occurs and switching back to the complex model for
typical situations. We instantiate the approach on a real-world data set of
taxi demand in New York City, which is prone to multiple drifts, e.g. the
weather phenomena of blizzards, resulting in a sudden decrease of taxi demand.
We are able to show that our suggested approach outperforms all regarded
baselines significantly.
