Naive Automated Machine Learning -- A Late Baseline for AutoML

TL;DR

Naive AutoML is a simple, interpretable baseline that leverages meta-knowledge, often outperforming complex black-box optimization methods in AutoML tasks.

Contribution

The paper introduces Naive AutoML, a straightforward approach that challenges sophisticated AutoML methods by using meta-knowledge and simple assumptions.

Findings

Naive AutoML often outperforms complex black-box solvers.

Naive AutoML offers interpretability and flexibility.

Complex AutoML methods do not always outperform the naive baseline.

Abstract

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.