Conference paper
deep-significance: Easy and Meaningful Signifcance Testing in the Age of Neural Networks
Abstract
A lot of Machine Learning (ML) and Deep Learning (DL) research is of anempirical nature. Nevertheless, statistical significance testing (SST) is still notwidely used. This endangers true progress, as seeming improvements over abaseline might be statistical flukes, leading follow-up research astray while wastinghuman and computational resources.
Here, we provide an easy-to-use packagecontaining different significance tests and utility functions specifically tailoredtowards research needs and usability
| Language: | English |
|---|---|
| Year: | 2022 |
| Proceedings: | ML Evaluation Standards Workshop at the Tenth International Conference on Learning Representations |
| Types: | Conference paper |
| ORCIDs: | Frellsen, Jes |
