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Table 2 The prediction performances of all machine learning models.

From: Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

 

10

20

30

 

Spe

Sen

Tot

AUR

Spe

Sen

Tot

AUR

Spe

Sen

Tot

AUR

LDA

70

78

73

0.80

76

89

80

0.87

82

83

82

0.88

QDA

85

50

73

0.82

88

44

73

0.80

91

72

84

0.84

CART

91

72

84

n.a.

76

83

78

n.a.

88

83

86

n.a.

1NN

91

72

84

n.a.

88

72

82

n.a.

85

72

80

n.a.

3NN

85

78

82

n.a.

94

72

86

n.a.

94

72

86

n.a.

5NN

94

72

86

n.a.

97

72

88

n.a.

97

72

88

n.a.

7NN

88

67

78

n.a.

88

78

84

n.a.

94

78

88

n.a.

9NN

94

50

78

n.a.

88

78

86

n.a.

94

78

88

n.a.

RF

97

83

92

0.93

97

83

92

0.95

97

83

92

0.94

ANN5

94

28

71

0.81

88

67

80

0.86

88

78

84

0.92

ANN10

100

33

76

0.82

94

78

88

0.94

91

72

84

0.92

ANN15

91

56

78

0.86

97

67

86

0.89

94

78

88

0.93

ANN20

91

56

78

0.88

94

72

86

0.96

94

78

88

0.93

SVM

87

78

83

0.89

100

72

90

0.94

94

78

88

0.92