Feb. 13, 2024, 5:44 a.m. | Yuetian Luo Rina Foygel Barber

cs.LG updates on arXiv.org arxiv.org

Algorithm evaluation and comparison are fundamental questions in machine learning and statistics -- how well does an algorithm perform at a given modeling task, and which algorithm performs best? Many methods have been developed to assess algorithm performance, often based around cross-validation type strategies, retraining the algorithm of interest on different subsets of the data and assessing its performance on the held-out data points. Despite the broad use of such procedures, the theoretical properties of these methods are not yet …

algorithm comparison cs.lg evaluation free machine machine learning math.st modeling performance questions retraining statistics stat.ml stat.th strategies tests the algorithm type validation

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