Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Sebastian Simon, Nikolay Kolyada, Christopher Akiki, Martin Potthast, Benno Stein, Norbert Siegmund
Abstract
The success of machine learning (ML) models depends on careful experimentation and optimization of their hyperparameters. Tuning can affect the reliability and accuracy of a trained model and is the subject of ongoing research. However, little is known on whether and how hyperparameters are used and optimized in research practice. This lack of knowledge not only limits the adoption of best practices for tuning in research, but also affects the reproducibility of published results. Our research systematically analyzes the use and tuning of hyperparameters in ML publications. For this, we analyze 2000 code repositories and their associated research papers from Papers with Code. We compare the use and tuning of hyperparameters of three widely used ML libraries: scikit-learn, TensorFlow, and PyTorch. Our results show that the most of the available hyperparameters remain untouched, and those that have been changed use constant values. In particular, there is a significant difference between tuning hyperparameters and the reporting about it in the corresponding research papers. Our results suggest that there is a need for improved research and reporting practices when using ML methods to improve the reproducibility of published results.