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.
Felix Mohr (Universidad de La Sabana)
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