Diagnostic accuracy of gradient boosted model.
- Marcia Cristina T. dos Santos (5920844)
- Dieter Scheller (675472)
- Claudia Schulte (125865)
- Irene R. Mesa (5920847)
- Peter Colman (4401316)
- Sarah R. Bujac (5920850)
- Rosie Bell (5920853)
- Caroline Berteau (5920856)
- Luis Tosar Perez (5920859)
- Ingolf Lachmann (709107)
- Daniela Berg (125872)
- Walter Maetzler (125849)
- Andre Nogueira da Costa (320345)
Publication date
November 2018
DOI Publisher
Public Library of Science (PLoS)Abstract
(A) Predictive probabilities of PD from the most promising GBM. The horizontal line corresponds to a predictive probability cut-off of 0.518 to classify PD and control. (B) Corresponding ROC curves, showing the AUC, optimal cut-off, sensitivity and specificity of the test ROC curves of model created by gradient boosted regression. (C) Graph of most influencialfrequent variables. (D) Example of a decision tree.</p
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