deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
- Dennis Thomas Ulmer,
- ,
- Jes Frellsen
Research Output:
Contribution to conference - NOT published in proceeding or journal
Paper
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Contribution to conference - NOT published in proceeding or journal
Paper
Peer-reviewOriginal language
EnglishPublication milestones
- Published - 29/04/2022
Publication status
Published - 29/04/2022
Abstract
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endangers true progress, as seeming improvements over a baseline might be statistical flukes, leading follow-up research astray while wasting human and computational resources. Here, we provide an easy-to-use package containing different significance tests and utility functions specifically tailored towards research needs and usability.
Access to documents
Final published version, 895.71 KB
