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

arXiv: https://arxiv.org/abs/2009.01564 · HTML: https://mlsanity.com/paper/2009.01564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [The Potential of AutoML for Recommender Systems](https://mlsanity.com/paper/2402.04453/) (2024)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims](https://mlsanity.com/paper/2107.10495/) (2021)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)

## Abstract (arXiv)

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.