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.

翻译：回归模型在众多现实应用中至关重要。然而在实践中，目标值并非总是精确已知的；相反，它们可能以可接受值的区间形式表示。这一挑战推动了区间回归模型的发展。本研究对现有区间回归模型进行了全面综述，并引入了替代模型以进行比较分析。通过在真实数据集和合成数据集上进行实验，为模型性能提供了广泛的视角。结果表明，不存在普遍最优的单一模型，这突显了针对每个具体场景选择最合适模型的重要性。