deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks

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 astra...

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Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Ulmer, Dennis, Hardmeier, Christian, Frellsen, Jes
Format: Article
Language:English
Subjects:
Deep learning
Human wastes
Machine learning
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422