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In this work we present classifier patching, an approach for adapting an existing black-box classification model to new data. Instead of creating a new model, patching infers regions in the instance space where the existing model is error-prone by training a classifier on the previously misclassified data. It then learns a specific model to determine the error regions, which allows to patch the old model’s predictions for them. Patching relies on a strong, albeit unchangeable, existing base classifier, and the idea that the true labels of seen instances will be available in batches at some point in time after the original classification. We experimentally evaluate our approach, and show that it meets the original design goals. Moreover, we compare our approach to existing methods from the domain of ensemble stream classification in both concept drift and transfer learning situations. Patching adapts quickly and achieves high classification accuracy, outperforming state-of-the-art competitors in either adaptation speed or accuracy in many scenarios.