"Accuracy-on-the-line" is a widely observed phenomenon in machine learning,
where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD)
data is positively correlated across different hyperparameters and data
configurations. But when does this useful relationship break down? In this
work, we explore its robustness. The key observation is that noisy data and the
presence of nuisance features can be sufficient to shatter the
Accuracy-on-the-line phenomenon. In these cases, ID and OOD accuracy can become
negatively correlated, leading to "Accuracy-on-the-wrong-line". This phenomenon
can also occur in the presence of spurious (shortcut) features, which tend to
overshadow the more complex signal (core, non-spurious) features, resulting in
a large nuisance feature space. Moreover, scaling to larger datasets does not
mitigate this undesirable behavior and may even exacerbate it. We formally
prove a lower bound on Out-of-distribution (OOD) error in a linear
classification model, characterizing the conditions on the noise and nuisance
features for a large OOD error. We finally demonstrate this phenomenon across
both synthetic and real datasets with noisy data and nuisance features.

通过研究机器学习中的 “在线准确性” 现象，我们发现噪声数据和干扰特征会破坏准确性与超参数和数据配置之间的正相关关系，导致准确性与超参数和数据配置之间出现负相关关系。这种现象也可能出现在含有假特征的情况下，假特征会凸显出复杂信号特征，导致大量干扰特征空间。此外，将数据扩展到更大规模并不能减轻这种不良行为，甚至可能加剧它。我们通过线性分类模型在真实和合成数据集上进行验证，并对噪声数据和干扰特征的条件下的 Out-of-distribution (OOD) 错误证明了下界。