MementoML：选定机器学习算法配置在OpenML100数据集上的性能
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
摘要：
为机器学习算法找到最佳超参数通常可以显著提高其性能。但是，如何以一种有效的方式选择它们呢？在本文中，我们提出了生成基准数据的协议，这些基准数据描述了具有不同超参数配置的不同ML算法的性能。以这种方式收集的数据用于研究影响算法性能的因素。这一收集是为了EPP研究中提出的研究目的而准备的。我们测试了算法的性能在稠密网格的超参数。测试数据集和超参数是在任何算法运行之前选择的，并且没有改变。这是一种不同于通常用于超参数调整的方法，其中候选超参数的选择取决于先前获得的结果。然而，这样的选择允许系统地分析来自各个超参数的性能灵敏度。这就产生了一个关于这些基准的全面数据集，我们愿意与大家分享。我们希望，计算和收集的结果可能会有助于其他研究人员。本文介绍了收集数据的方式。在这里，您可以在39个OpenML数据集上找到7种流行的机器学习算法的基准测试。形成该基准的详细数据可在https://www.kaggle.com/mi2datalab/mementoml上获得。
英文摘要：
Finding optimal hyperparameters for the machine learning algorithm can often significantly improve its performance. But how to choose them in a time-efficient way? In this paper we present the protocol of generating benchmark data describing the performance of different ML algorithms with different hyperparameter configurations. Data collected in this way is used to study the factors influencing the algorithm's performance. This collection was prepared for the purposes of the study presented in the EPP study. We tested algorithms performance on dense grid of hyperparameters. Tested datasets and hyperparameters were chosen before any algorithm has run and were not changed. This is a different approach than the one usually used in hyperparameter tuning, where the selection of candidate hyperparameters depends on the results obtained previously. However, such selection allows for systematic analysis of performance sensitivity from individual hyperparameters. This resulted in a comprehensive dataset of such benchmarks that we would like to share. We hope, that computed and collected result may be helpful for other researchers. This paper describes the way data was collected. Here you can find benchmarks of 7 popular machine learning algorithms on 39 OpenML datasets. The detailed data forming this benchmark are available at: https://www.kaggle.com/mi2datalab/mementoml.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
