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. 2020 Feb 13;15:3. doi: 10.1186/s13062-020-0259-4

Table 2.

Results obtained for RF and NBM2 classifiers using different class balancing strategies

balancing strategy classifier MCC cv MCC val
adasyn RF 0.63 (0.60, 0.66) 0.12
oversampled_all RF 0.69 (0.65, 0.71) -0.13
oversampled_minority RF 0.69 (0.65, 0.71) -0.13
smote RF 0.63 (0.60, 0.66) 0.02
smote_svm RF 0.61 (0.59, 0.65) -0.09
smote_borderline1 RF 0.61 (0.58, 0.64) -0.04
smote_borderline2 RF 0.59 (0.55, 0.63) -0.07
adasyn NBM2 0.07 (0.03, 0.10) 0.02
oversampled_all NBM2 0.24 (0.19, 0.29) -0.02
oversampled_minority NBM2 0.23 (0.19, 0.28) 0.07
smote NBM2 0.20 (0.15, 0.25) -0.2
smote_svm NBM2 0.24 (0.20, 0.29) 0.1
smote_borderline1 NBM2 0.23 (0.19, 0.29) -0.11
smote_borderline2 NBM2 0.11 (0.06, 0.16) -0.01

Boldface indicates the best performance of RF or NBM2 models either in cross validation or in validation