TITLE: Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark 

ABSTRACT: 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.
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.
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark
http://arxiv.org/abs/2009.01564v2