Replacing the Ex-Def Baseline in AutoML by Naive AutoML

Felix Mohr, Marcel Wever

Keywords: Automated Machine Learning, Independency Assumption, Baselines

TL;DR: Introducing a simple yet strong baseline to evaluate AutoML approaches against.

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 like Bayesian Optimization, Genetic Algorithms, or Tree Search. These approaches are almost never compared to simple baselines to see how much they improve over simple but easy to implement approaches. We present Naive AutoML, a very simple baseline for AutoML that exploits meta-knowledge about machine learning problems and makes simplifying, yet, effective assumptions to quickly come to high-quality solutions. In 1h experiments, state of the art approaches can hardly improve over Naive AutoML which in turn comes along with advantages such as interpretability and flexibility.

Ethics Statement: We are currently not aware of ethical conflicts. AutoML in general should be concerned about computational resources. Our baseline is in fact a cheap alternative to other state-of-the-art techniques. So it might even help to abstain from resource intense approaches and favor more informed ones.