Imbalance in Regression Datasets
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914685036527616 |
|---|---|
| author | Kowatsch, Daniel Müller, Nicolas M. Tscharke, Kilian Sperl, Philip Bötinger, Konstantin |
| author_facet | Kowatsch, Daniel Müller, Nicolas M. Tscharke, Kilian Sperl, Philip Bötinger, Konstantin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Imbalance in Regression Datasets Kowatsch, Daniel Müller, Nicolas M. Tscharke, Kilian Sperl, Philip Bötinger, Konstantin Machine Learning Artificial Intelligence 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. |
| title | Imbalance in Regression Datasets |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.11963 |