# Parametric PDF for Goodness of Fit

arXiv: https://arxiv.org/abs/2210.14005 · HTML: https://mlsanity.com/paper/2210.14005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Goodness of Fit Metrics for Multi-class Predictor](https://mlsanity.com/paper/2208.05651/) (2022)

## Built on by (most cited first, 1 of 1)

- [ML Study of MaliciousTransactions in Ethereum](https://mlsanity.com/paper/2408.08749/) (2024)

## Abstract (arXiv)

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.