# A unifying approach on bias and variance analysis for classification

arXiv: https://arxiv.org/abs/2101.01765 · HTML: https://mlsanity.com/paper/2101.01765/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Aggregation of Classifiers: A Justifiable Information Granularity Approach](https://mlsanity.com/paper/1703.05411/) (2017)
- [Ensembles of Random Sphere Cover Classifiers](https://mlsanity.com/paper/1409.4936/) (2014)
- [Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles](https://mlsanity.com/paper/1711.05482/) (2017)

## Abstract (arXiv)

Standard bias and variance (B&V) terminologies were originally defined for the regression setting and their extensions to classification have led to several different models / definitions in the literature. In this paper, we aim to provide the link between the commonly used frameworks of Tumer & Ghosh (T&G) and James. By unifying the two approaches, we relate the B&V defined for the 0/1 loss to the standard B&V of the boundary distributions given for the squared error loss. The closed form relationships provide a deeper understanding of classification performance, and their use is demonstrated in two case studies.