Table 3 Results using different classification algorithms on a split of 80–20% for training and test, based on their F-Measure, AUC-ROC, and AU-PRC

NaiveBayesMultinomial

0.59

0.66

0.68

SimpleLogistic

0.57

0.62

0.63

MultilayerPerceptron

0.65

0.66

0.66

Logistic

0.62

0.67

0.69

VotedPerceptron

0.61

0.60

0.60

SVM

0.63

0.60

0.59

IBK

0.66

0.63

0.61

AdaBoostM1

0.58

0.65

0.63

ClassificationViaRegression

0.62

0.67

0.69

PART

0.66

0.67

0.65

Bagging+REPTree

0.70

0.69

0.69

RandomForest

0.71

0.73

0.74

J48

0.68

0.69

0.67

EXTRA Tree

0.69

0.66

0.63