Luo Gang
Department of Biomedical Informatics and Medical Education, University of Washington UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047 Seattle, WA 98195, USA,
SIGKDD Explor. 2018 Dec;20(2):1-12. doi: 10.1145/3299986.3299988.
Progress indicators are desirable for machine learning model building that often takes a long time, by continuously estimating the remaining model building time and the portion of model building work that has been finished. Recently, we proposed a high-level framework using system approaches to support non-trivial progress indicators for machine learning model building, but offered no detailed implementation technique. It remains to be seen whether it is feasible to provide such progress indicators. In this paper, we fill this gap and give the first demonstration that offering such progress indicators is viable. We describe detailed progress indicator implementation techniques for three major, supervised machine learning algorithms. We report an implementation of these techniques in Weka.
对于通常需要很长时间的机器学习模型构建而言，通过持续估计剩余的模型构建时间以及已完成的模型构建工作部分，进度指标是很有必要的。最近，我们提出了一个使用系统方法的高级框架，以支持机器学习模型构建的重要进度指标，但未提供详细的实现技术。提供这样的进度指标是否可行仍有待观察。在本文中，我们填补了这一空白，并首次证明提供此类进度指标是可行的。我们描述了三种主要的监督式机器学习算法的详细进度指标实现技术。我们报告了这些技术在Weka中的实现情况。
