The use of machine learning is increasing in clinical psychology, yet it is unclear whether these approaches enhance the prediction of clinical outcomes. Several studies show that machine-learning algorithms outperform traditional linear models. However, many studies that have found such an advantage use the same algorithm, random forests with the optimism-corrected bootstrap, for internal validation. Through both a simulation and empirical example, we demonstrate that the pairing of nonlinear, flexible machine-learning approaches, such as random forests with the optimism-corrected bootstrap, provide highly inflated prediction estimates. We find no advantage for properly validated machine-learning models over linear models.

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夸大预测性能的证据：对机器学习和自杀研究的评论

机器学习在临床心理学中的使用越来越多，但尚不清楚这些方法是否能增强对临床结果的预测。多项研究表明，机器学习算法优于传统的线性模型。然而，许多发现这种优势的研究使用相同的算法，即带有乐观校正引导程序的随机森林，用于内部验证。通过模拟和实证示例，我们证明了非线性、灵活的机器学习方法的配对，例如随机森林与乐观校正引导程序，提供了高度膨胀的预测估计。我们发现正确验证的机器学习模型没有优于线性模型的优势。