Table 2 The Classification Performance of Different Classifiers.

From: Computational characterization and identification of human polycystic ovary syndrome genes

Classifier

Precision

Recall

F1

AUC

KNN (K = 7)

0.77

0.69

0.73

0.78

Decision tree

0.76

0.74

0.75

0.79

SVM (liner)

0.81

0.71

0.75

0.80

SVM (polynomial d = 3)

0.49

0.73

0.58

0.57

SVM (RBF)

0.79

0.68

0.73

0.79

  1. SVM (linear), SVM (polynomial d = 3) and SVM (RBF) means the kernel function of SVM is linear, polynomial, and radial basis function, respectively.
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