Interval Regression: A Comparative Study with Proposed Models
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918232699437056 |
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| author | Nguyen, Tung L Hocking, Toby Dylan |
| author_facet | Nguyen, Tung L Hocking, Toby Dylan |
| contents | Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interval Regression: A Comparative Study with Proposed Models Nguyen, Tung L Hocking, Toby Dylan Machine Learning Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario. |
| title | Interval Regression: A Comparative Study with Proposed Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.02011 |