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.
All Science Journal Classification (ASJC) codes
- Clinical Psychology
- clinical psychology
- data mining
- machine learning