Parametric PDF for Goodness of Fit
arXiv:2210.14005v21 citationsh-index: 11
AI Analysis
This work addresses classification model evaluation for researchers and practitioners, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackled the problem of evaluating goodness of fit in classification by introducing a parametric PDF framework to enrich traditional confusion matrix methods with risk evaluation and stability analysis tools, but no concrete results or numbers were provided.
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.