  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.
