Tuning machine learning models with Bayesian optimization (BO) is a successful strategy to find good hyperparameters. BO defines an iterative procedure where a cross-validated metric is evaluated on promising hyperparameters. In practice, however, an improvement of the validation metric may not translate in better predictive performance on a test set, especially when tuning models trained on small datasets. In other words, unlike conventional wisdom dictates, BO can overfit. In this paper, we carry out the first systematic investigation of overfitting in BO and demonstrate that this issue is serious, yet often overlooked in practice. We propose a novel criterion to early stop BO, which aims to maintain the solution quality while saving the unnecessary iterations that can lead to overfitting. Experiments on real-world hyperparameter optimization problems show that our approach effectively meets these goals and is more adaptive comparing to baselines.
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