Philip Buczak, Andreas Groll, Markus Pauly, Jakob Rehof, Daniel Horn
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引用次数: 0
Abstract
Hyperparameter tuning is one of the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the machine learning method has to be fitted a fixed number of k times on different training datasets. The respective mean performance of the k fits is then used as performance estimator. Many hyperparameter settings could be discarded after less than k resampling iterations if they are clearly inferior to high-performing settings. However, resampling is often performed until the very end, wasting a lot of computational effort. To this end, we propose the sequential random search (SQRS) which extends the regular random search algorithm by a sequential testing procedure aimed at detecting and eliminating inferior parameter configurations early. We compared our SQRS with regular random search using multiple publicly available regression and classification datasets. Our simulation study showed that the SQRS is able to find similarly well-performing parameter settings while requiring noticeably fewer evaluations. Our results underscore the potential for integrating sequential tests into hyperparameter tuning.
超参数调整是机器学习中最耗时的部分之一。尽管现代优化算法可以最大限度地减少所需的评估次数,但对单个设置的评估仍可能非常昂贵。通常会使用重采样技术,即在不同的训练数据集上对机器学习方法进行固定次数的 k 次拟合。然后将 k 次拟合各自的平均性能作为性能估计值。如果许多超参数设置明显不如高性能设置,那么可以在少于 k 次的重采样迭代后将其舍弃。然而,重采样往往要到最后才进行,浪费了大量的计算资源。为此,我们提出了顺序随机搜索(SQRS),它通过一个顺序测试程序扩展了常规随机搜索算法,旨在及早检测和消除劣质参数配置。我们使用多个公开的回归和分类数据集对 SQRS 和常规随机搜索进行了比较。我们的模拟研究表明,SQRS 能够找到类似的性能良好的参数设置,而所需的评估次数却明显减少。我们的结果强调了将顺序测试整合到超参数调整中的潜力。
期刊介绍:
AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.