Decisions under drift: Adapting binary decision thresholds to drifts in test distribution

Most predictive models built for binary decision problems compute a real valued score as an intermediate step and then apply a threshold on this score to make a final decision. Conventionally, the threshold is chosen which optimizes a desired performance metric (such as accuracy, F-score, precision@k, recall@k, etc.) on the training set. However very often in practice it so happens that the same threshold when applied to a test set, results in a sub-optimal performance because of drift in test distribution. In this work we propose a method that adaptively changes the threshold such that the optimal performance achieved on the training set is maintained. The method is completely unsupervised and is based on fitting a parametric mixture model to the test scores and choosing the threshold that optimizes a performance metric based on the corresponding parametric approximation.