# Goodness of Fit Metrics for Multi-class Predictor

arXiv: https://arxiv.org/abs/2208.05651 · HTML: https://mlsanity.com/paper/2208.05651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 11 Aug 2022.

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

- [Parametric PDF for Goodness of Fit](https://mlsanity.com/paper/2210.14005/) (2022)

## Abstract (arXiv)

The multi-class prediction had gained popularity over recent years. Thus measuring fit goodness becomes a cardinal question that researchers often have to deal with. Several metrics are commonly used for this task. However, when one has to decide about the right measurement, he must consider that different use-cases impose different constraints that govern this decision. A leading constraint at least in real world multi-class problems is imbalanced data: Multi categorical problems hardly provide symmetrical data. Hence, when we observe common KPIs (key performance indicators), e.g., Precision-Sensitivity or Accuracy, one can seldom interpret the obtained numbers into the model's actual needs. We suggest generalizing Matthew's correlation coefficient into multi-dimensions. This generalization is based on a geometrical interpretation of the generalized confusion matrix.