An Open Source AutoML Benchmark
This provides a standardized tool for researchers and practitioners to evaluate AutoML systems, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the difficulty of comparing AutoML systems by introducing an open-source benchmark framework that follows best practices and avoids common mistakes, and they used it to compare 4 AutoML systems across 39 datasets.
In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The framework is open-source, uses public datasets and has a website with up-to-date results. We use the framework to conduct a thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.