Performance metrics (AUC, CA, F1, Recall, MCC) of Boosting, Stacking and Voting Ensemble Learnings with SET, ADASYN and BS balancing methods in internal test, 2011 and 2014 datasets.

<p>Note: 95% CIs (AUC: Delong method; CA/F1/Recall/MCC: 1000 bootstrap samples). Model comparisons: one-way ANOVA with Bonferroni correction; *p < 0.05 vs. Stacking-ADASYN, #p < 0.05 vs. Voting-ADASYN. Key metrics for Boosting-ADASYN: internal test AUC = 0.814 (0.782–0.846), MCC = 0.493...

Full description

Saved in:

Bibliographic Details
Main Author:	Kai Wang (21246) (author)
Published:	2025
Subjects:	Sociology Developmental Biology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapley additive explanations chinese older adults body shape index abnormal body shape 766 &# 8211 early anxiety identification absi outperformed bmi 2008 &# 8211 anxiety symptoms xlink "> voting ), top predictors study provides study aimed stronger effects reported health positively associated interpretable tool internal validation feature selection external validation ensemble learning 79 %). 772 ). 37 %) 038 ),
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Performance metrics (AUC, CA, F1, Recall, MCC) of Boosting, Stacking and Voting Ensemble Learnings with SET, ADASYN and BS balancing methods in internal test, 2011 and 2014 datasets.

Similar Items