Naive automated machine learning

Abstract An essential task of automated machine learning ( $$\text {AutoML}$$ AutoML ) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated $$\text {black-box}$$ black-box optimization techniques such as Bayesian optimization, grammar-based genetic algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present $$\text {Naive AutoML}$$ Naive Au