# Imbalance in Regression Datasets

arXiv: https://arxiv.org/abs/2402.11963 · HTML: https://mlsanity.com/paper/2402.11963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 19 Feb 2024.

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

- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)

## Built on by (most cited first, 2 of 2)

- [Model-agnostic Mitigation Strategies of Data Imbalance for Regression](https://mlsanity.com/paper/2506.01486/) (2025)
- [Instance Hardness-Based Relevance for Imbalanced Regression](https://mlsanity.com/paper/2607.20173/) (2026)

## Abstract (arXiv)

For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important problem which has so far been overlooked: Due to under- and over-representations in a data set's target distribution, regressors are prone to degenerate to naive models, systematically neglecting uncommon training data and over-representing targets seen often during training. We analyse this problem theoretically and use resulting insights to develop a first definition of imbalance in regression, which we show to be a generalisation of the commonly employed imbalance measure in classification. With this, we hope to turn the spotlight on the overlooked problem of imbalance in regression and to provide common ground for future research.