论文标题
每个参数高：用于机器学习算法的高参数调整的大规模研究
High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
论文作者
论文摘要
机器学习中的超参数（ML）受到了相当多的关注，而高参数调整已被视为ML管道中的重要一步。但是，这是有用的调音呢？虽然先前已经进行了较小的实验，但本文中，我们进行了大规模研究，特别是涉及26个ML算法，250个数据集（回归以及二进制和多项式分类），6个得分指标和28,857,600算法运行。分析结果我们得出的结论是，对于许多ML算法，我们不应该期望平均而言，高参数调整会获得可观的收益，但是，可能有一些数据集的默认超参数的性能较差，而后者对于某些算法而言是比其他算法更真实的。通过定义组合算法的累积统计数据的单个HP_SCORE值，我们能够从预期从高参数调谐到预期获得最低收益的26个ML算法进行排名的26毫升算法。我们认为，这样的研究可能会为ML从业者提供整体服务。
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But just how useful is said tuning? While smaller-scale experiments have been previously conducted, herein we carry out a large-scale investigation, specifically, one involving 26 ML algorithms, 250 datasets (regression and both binary and multinomial classification), 6 score metrics, and 28,857,600 algorithm runs. Analyzing the results we conclude that for many ML algorithms we should not expect considerable gains from hyperparameter tuning on average, however, there may be some datasets for which default hyperparameters perform poorly, this latter being truer for some algorithms than others. By defining a single hp_score value, which combines an algorithm's accumulated statistics, we are able to rank the 26 ML algorithms from those expected to gain the most from hyperparameter tuning to those expected to gain the least. We believe such a study may serve ML practitioners at large.
