Naive Automated Machine Learning -- A Late Baseline for AutoML
This work challenges existing AutoML tools by providing a strong, interpretable baseline, which is incremental but poses a significant problem for researchers and practitioners in machine learning automation.
The paper tackles the problem of Automated Machine Learning (AutoML) by proposing Naive AutoML, a simple method that uses meta-knowledge and assumptions to find high-quality solutions, and empirically shows it often matches or outperforms sophisticated black-box solvers.
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.