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}
}
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