混淆泄漏：机器学习中的混淆去除导致泄漏
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
摘要：
数据分析的机器学习（ML）方法现在被广泛应用于包括流行病学和医学在内的许多领域。要应用这些方法，必须首先消除混淆，这通常是在应用ML之前通过线性回归按特征去除其方差来完成的。在这里，我们展示了这种常见的混淆去除方法，这种方法会导致ML模型产生误导性的结果。具体地说，这种常见的去干扰方法可能会泄漏信息，使得当随后应用非线性ML方法时，无效或中等影响被放大到近乎完美的预测。我们确定和评估这种混淆泄漏的可能机制，并提供切实可行的指导，以减轻其负面影响。我们通过分析一个临床数据集来证明混杂泄漏在现实世界中的重要性，在该数据集中，预测注意缺陷多动障碍（ADHD）与抑郁症作为混杂因素的准确性被高估。我们的研究结果对ML工作流的实现和部署具有广泛的影响，并警告不要天真地使用标准混淆删除方法。
英文摘要：
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be removed as is commonly done by featurewise removal of their variance by linear regression before applying ML. Here, we show this common approach to confound removal biases ML models, leading to misleading results. Specifically, this common deconfounding approach can leak information such that what are null or moderate effects become amplified to near-perfect prediction when nonlinear ML approaches are subsequently applied. We identify and evaluate possible mechanisms for such confound-leakage and provide practical guidance to mitigate its negative impact. We demonstrate the real-world importance of confound-leakage by analyzing a clinical dataset where accuracy is overestimated for predicting attention deficit hyperactivity disorder (ADHD) with depression as a confound. Our results have wide-reaching implications for implementation and deployment of ML workflows and beg caution against naïve use of standard confound removal approaches.
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