用于回归的反事实残差数据增强

arXiv:2606.28460v1 Announce Type: new Abstract: Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations.受数据增强对视觉和语言的影响的启发，我们提出了一种用于表格回归的新型反事实残差数据增强（CRDA）技术。 Our key insight is that once a regressor has modeled the systematic component of the data, the remaining noise can be viewed as an invariant residual that remains stable under small perturbations of carefully selected features. We exploit this residual invariance to generate new, yet realistic, training samples, effectively expanding the dataset without requiring additional real data.我们的方法与模型无关，并且很容易适用于各种类型的回归量。 In experiments across datasets from a variety of benchmark repositories, on average, CRDA reduces an MLP Regressor's MSE by 22.9% and an XGBoost Regressor's MSE by 6.4%.与现有最先进的数据生成器和增强技术相比，CRDA 在 MSE 降低方面始终表现出色。 By adding principled counterfactual variations to the training data, our method offers a simple and efficient remedy for noise-prone, small-sample regression settings.