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Given a classifier ensemble and a set of examples to be classified, manyexamples may be confidently and accurately classified after only a subset ofthe base models in the ensemble are evaluated. This can reduce both meanlatency and CPU while maintaining the high accuracy of the original ensemble.To achieve such gains, we propose jointly optimizing a fixed evaluation orderof the base models and early-stopping thresholds. Our proposed objective is acombinatorial optimization problem, but we provide a greedy algorithm thatachieves a 4-approximation of the optimal solution for certain cases. For thosecases, this is also the best achievable polynomial time approximation boundunless $P = NP$. Experiments on benchmark and real-world problems show that theproposed Quit When You Can (QWYC) algorithm can speed-up average evaluationtime by $2$x--$4$x, and is around $1.5$x faster than prior work. QWYC's jointoptimization of ordering and thresholds also performed better in experimentsthan various fixed orderings, including gradient boosted trees' ordering.

Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization