Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research

Ross Jacobucci, Andrew K. Littlefield, Alexander J. Millner, Evan M. Kleiman, Douglas Steinley

Research output: Contribution to journalComment/debate

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.

Original languageEnglish
Pages (from-to)129-134
Number of pages6
JournalClinical Psychological Science
Volume9
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • clinical psychology
  • data mining
  • machine learning
  • prediction
  • suicide

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