论文标题
汽车的表现能胜过人类吗？使用汽车基准对流行的OpenML数据集进行评估
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark
论文作者
论文摘要
在过去的几年中，自动化机器学习（AUTOML）引起了很多关注。话虽如此，出现了一个问题，即汽车是否可以胜过人类数据科学家取得的结果。本文比较了OpenML的12个不同流行数据集上的四个Automl框架；他们中有六个监督分类任务以及其他六个监督回归。此外，我们考虑了我们最近一个项目之一的真实数据集。结果表明，在12个OpenML任务中的7个中，自动化框架的性能要比机器学习社区更好或相等。
In the last few years, Automated Machine Learning (AutoML) has gained much attention. With that said, the question arises whether AutoML can outperform results achieved by human data scientists. This paper compares four AutoML frameworks on 12 different popular datasets from OpenML; six of them supervised classification tasks and the other six supervised regression ones. Additionally, we consider a real-life dataset from one of our recent projects. The results show that the automated frameworks perform better or equal than the machine learning community in 7 out of 12 OpenML tasks.
