TY  - JOUR
PY  - 2021//
TI  - Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research
JO  - Clinical psychological science
A1  - Jacobucci, R.
A1  - Littlefield, A.K.
A1  - Millner, A.J.
A1  - Kleiman, E.M.
A1  - Steinley, D.
SP  - 129
EP  - 134
VL  - 9
IS  - 1
N2  - 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>
LA  - en
SN  - 2167-7026
UR  - http://dx.doi.org/10.1177/2167702620954216
ID  - ref1
ER  -