# Naive Automated Machine Learning -- A Late Baseline for AutoML

arXiv: https://arxiv.org/abs/2103.10496 · HTML: https://mlsanity.com/paper/2103.10496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [An ADMM Based Framework for AutoML Pipeline Configuration](https://mlsanity.com/paper/1905.00424/) (2019)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [OBOE: Collaborative Filtering for AutoML Model Selection](https://mlsanity.com/paper/1808.03233/) (2018)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [Autostacker: A Compositional Evolutionary Learning System](https://mlsanity.com/paper/1803.00684/) (2018)
- [Automated Machine Learning with Monte-Carlo Tree Search](https://mlsanity.com/paper/1906.00170/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fast and Informative Model Selection using Learning Curve Cross-Validation](https://mlsanity.com/paper/2111.13914/) (2021)

## Abstract (arXiv)

Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset. AutoML has received enormous attention in the last decade and has been addressed with sophisticated black-box optimization techniques such as Bayesian Optimization, Grammar-Based Genetic Algorithms, and tree search algorithms. In contrast to those approaches, we present Naive AutoML, a very simple solution to AutoML that exploits important meta-knowledge about machine learning problems and makes simplifying, yet, effective assumptions to quickly come to high-quality solutions. While Naive AutoML can be considered a baseline for the highly sophisticated black-box solvers, we empirically show that those solvers are not able to outperform Naive AutoML; sometimes the contrary is true. On the other hand, Naive AutoML comes with strong advantages such as interpretability and flexibility and poses a strong challenge to current tools.