Table 4 Comparative analysis of learning classifiers.

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

89.97

87.78

90.94

94.45

89.34

0.876

0.953

RF

82.56

74.23

81.56

87.94

78.23

0.698

0.865

KNN

81.23

72.96

80.52

86.93

77.34

0.689

0.855

SVM

80.12

71.45

79.85

85.79

76.12

0.673

0.845

NB

79.54

71.12

79.12

85.47

74.56

0.667

0.838

LR

78.89

69.87

78.54

84.56

73.21

0.651

0.832

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