@article{ref1,
title="Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research",
journal="Clinical psychological science",
year="2021",
author="Jacobucci, R. and Littlefield, A.K. and Millner, A.J. and Kleiman, E.M. and Steinley, D.",
volume="9",
number="1",
pages="129-134",
abstract="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. © The Author(s) 2021.<p /><p>Language: en</p>",
language="en",
issn="2167-7026",
doi="10.1177/2167702620954216",
url="http://dx.doi.org/10.1177/2167702620954216"
}