Table 5 Performance evaluation of learning hypotheses using independent dataset.

From: A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

Classifier

ACC (%)

SN (%)

Precision (%)

SPE (%)

F1 Score (%)

MCC

AUC

XGBoost

84.87

77.76

84.32

90.34

81.56

0.741

0.888

RF

82.53

74.24

81.67

87.45

78.32

0.698

0.862

SVM

83.45

75.43

82.74

88.86

79.12

0.716

0.875

NB

82.32

78.20

80.54

84.41

81.32

0.708

0.864

LR

81.23

73.32

80.32

86.94

77.23

0.688

0.853

KNN

80.56

72.21

79.83

85.95

75.56

0.672

0.845

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