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Parametric PDF for Goodness of Fit

Machine Learning 2022-11-02 v2 Artificial Intelligence

Abstract

The goodness of fit methods for classification problems relies traditionally on confusion matrices. This paper aims to enrich these methods with a risk evaluation and stability analysis tools. For this purpose, we present a parametric PDF framework.

Cite

@article{arxiv.2210.14005,
  title  = {Parametric PDF for Goodness of Fit},
  author = {Natan Katz and Uri Itai},
  journal= {arXiv preprint arXiv:2210.14005},
  year   = {2022}
}
R2 v1 2026-06-28T04:27:51.308Z