Certainty-Validity: A Diagnostic Framework for Discrete Commitment Systems

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Authors: Datorien L. Anderson
Cited by: 0

Abstract

The following includes the paper for the Certainty-Validity (CVS / CyVy) evaluation metric for datasets. CyVy helps with ML research by allowing to guide the model towards being confidentally correct and minimize confidentally inaccurate. In the repository are the following: "certainty_validity.py", a python script that allows for using the evaluation metric. "Certainty-Validity: A Diagnostic Framework for Discrete Commitment Systems", the main working paper for this. "cvs-supplementary-data.zip", a zip file that contains the a full 30 epoch benchmark in progress and epochs, with an additional figure of the isolated phase diagram that is at exists at the bottom of the paper.

Fields of Study & Subjects

Machine Learning and Data Classification Adversarial Robustness in Machine Learning Advanced Neural Network Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026
Document type
Preprint