Parametric PDF for Goodness of Fit
Natan Katz, Uri Itai
TL;DR
This paper introduces a parametric probability density function framework to enhance goodness of fit methods in classification, incorporating risk evaluation and stability analysis tools.
Contribution
It proposes a novel parametric PDF approach that extends traditional confusion matrix-based goodness of fit methods with additional risk and stability assessments.
Findings
Provides a new framework for goodness of fit analysis
Enables risk evaluation in classification models
Offers stability analysis tools for model assessment
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMulti-Criteria Decision Making