Abstract: Herein are acceleration and increased reliability based on classification and scoring techniques for machine learning that compare two similar datasets of different ages to detect data drift without a predefined drift threshold. Various subsets are randomly sampled from the datasets. The subsets are combined in various ways to generate subsets of various age mixtures. In an embodiment, ages are permuted and drift is detected based on whether or not fitness scores indicate that an age binary classifier is confused. In an embodiment, an anomaly detector measures outlier scores of two subsets of different age mixtures. Drift is detected when the outlier scores diverge. In a two-arm bandit embodiment, iterations randomly alternate between both datasets based on respective probabilities that are adjusted by a bandit reward based on outlier scores from an anomaly detector. Drift is detected based on the probability of the younger dataset.