Table 6 Performance of different boosting algorithms under \(CV_1.\)

	Dataset	XGBoost	AdaBoost	CatBoost	LightGBM	LDA-GARB
Precision	Dataset 1	0.8285 ± 0.0450	0.8014 ± 0.0467	0.8436 ± 0.0386	0.8359 ± 0.0537	0.8636\({\varvec{\pm }}\)0.0450
Precision	Dataset 2	0.9094 ± 0.0203	0.8945 ± 0.0250	0.9200 ± 0.0224	0.9007 ± 0.0240	0.9344\({\varvec{\pm }}\)0.0147
Recall	Dataset 1	0.7627 ± 0.0615	0.7407 ± 0.0855	0.7772\({\varvec{\pm }}\)0.0541	0.7624 ± 0.0668	0.7682 ± 0.0452
Recall	Dataset 2	0.8739 ± 0.0305	0.8605 ± 0.0432	0.8761\({\varvec{\pm }}\)0.0427	0.8755 ± 0.0346	0.8680 ± 0.0429
Accuracy	Dataset 1	0.8083 ± 0.0325	0.7946 ± 0.0312	0.8191 ± 0.0322	0.8190 ± 0.0268	0.8282\({\varvec{\pm }}\)0.0338
Accuracy	Dataset 2	0.8935 ± 0.0241	0.8795 ± 0.0263	0.8997 ± 0.0288	0.8895 ± 0.0293	0.9036\({\varvec{\pm }}\)0.0281
F1-score	Dataset 1	0.7925 ± 0.0410	0.7681 ± 0.0626	0.8077 ± 0.0352	0.7964 ± 0.0550	0.8117\({\varvec{\pm }}\)0.0312
F1-score	Dataset 2	0.8910 ± 0.0205	0.8764 ± 0.0248	0.8969 ± 0.0266	0.8876 ± 0.0251	0.8995\({\varvec{\pm }}\)0.0266
AUC	Dataset 1	0.8938 ± 0.0263	0.8528 ± 0.0386	0.9099 ± 0.0214	0.8988 ± 0.0277	0.9180\({\varvec{\pm }}\)0.0219
AUC	Dataset 2	0.9629 ± 0.0125	0.9421 ± 0.0265	0.9660 ± 0.0133	0.9574 ± 0.0212	0.9716\({\varvec{\pm }}\)0.0134
AUPR	Dataset 1	0.8799 ± 0.0421	0.8558 ± 0.0591	0.9053 ± 0.0275	0.8939 ± 0.0582	0.9160\({\varvec{\pm }}\)0.0286
AUPR	Dataset 2	0.9660 ± 0.0097	0.9495 ± 0.0189	0.9674 ± 0.0111	0.9628 ± 0.0135	0.9723\({\varvec{\pm }}\)0.0101

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