TY - GEN

T1 - Replacing the Ex-Def Baseline in AutoML by Naive AutoML

AU - Mohr, Felix

AU - Wever, Marcel

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of the 8th ICML Workshop on Automated Machine Learning

ER -