Replacing the Ex-Def Baseline in AutoML by Naive AutoML

Mohr, Felix und Wever, Marcel (July 2021): Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual, 2021-07-23.

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.

• Item Type: Conference or Workshop Item (Paper)
• Keywords: Automated Machine Learning, Independency Assumption, Baselines
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English