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The Ethereal
Parametric PDF for Goodness of Fit
October 25, 2022 ยท Declared Dead ยท ๐ Advances in Artificial Intelligence and Machine Learning
"No code URL or promise found in abstract"
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Authors
Natan Katz, Uri Itai
arXiv ID
2210.14005
Category
cs.LG: Machine Learning
Cross-listed
cs.AI
Citations
1
Venue
Advances in Artificial Intelligence and Machine Learning
Last Checked
4 months ago
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.
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