Calibration: the Achilles heel of predictive analytics

Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and poten...

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Veröffentlicht in:	BMC medicine Jg. 17; H. 1; S. 230 - 7
Hauptverfasser:	Van Calster, Ben, McLernon, David J., van Smeden, Maarten, Wynants, Laure, Steyerberg, Ewout W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 16.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Adult Aged Algorithms Analysis Artificial intelligence Biomedicine Calibration Calibration - standards Data mining Decision analysis Decision making Embryos Estimates Health care policy Health counseling Heterogeneity Humans In vitro fertilization Learning algorithms Machine learning Machine Learning - standards Male Medicine Medicine & Public Health Middle Aged Model performance Opinion Overfitting Patients Prediction models Predictive analytics Predictive Value of Tests Regression analysis Risk prediction models Shared decision making Heterogeneity Risk prediction models Model performance Calibration Predictive analytics Overfitting
ISSN:	1741-7015, 1741-7015
Online-Zugang:	Volltext
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