|
Dataset
|
ML method
|
AUC
|
CA
|
F1
|
Precision
|
Recall
|
|---|
|
Training set
|
SGD
|
0.647
|
0.709
|
0.715
|
0.723
|
0.709
|
|
kNN
|
0.654
|
0.722
|
0.698
|
0.690
|
0.722
|
|
SVM
|
0.785
|
0.795
|
0.791
|
0.788
|
0.795
|
|
DT
|
0.710
|
0.756
|
0.760
|
0.764
|
0.756
|
|
RF
|
0.814
|
0.838
|
0.827
|
0.832
|
0.838
|
|
Adaboost
|
0.785
|
0.792
|
0.788
|
0.785
|
0.792
|
|
ANN
|
0.621
|
0.737
|
0.671
|
0.685
|
0.737
|
|
LR
|
0.746
|
0.776
|
0.757
|
0.757
|
0.761
|
|
NB
|
0.632
|
0.686
|
0.694
|
0.711
|
0.686
|
|
Test set
|
SGD
|
0.627
|
0.682
|
0.694
|
0.712
|
0.682
|
|
kNN
|
0.574
|
0.745
|
0.636
|
0.555
|
0.745
|
|
SVM
|
0.669
|
0.747
|
0.712
|
0.710
|
0.747
|
|
DT
|
0.544
|
0.680
|
0.679
|
0.678
|
0.680
|
|
RF
|
0.739
|
0.767
|
0.731
|
0.739
|
0.767
|
|
Adaboost
|
0.614
|
0.708
|
0.707
|
0.707
|
0.708
|
|
ANN
|
0.647
|
0.694
|
0.696
|
0.697
|
0.694
|
|
LR
|
0.656
|
0.733
|
0.694
|
0.688
|
0.733
|
|
NB
|
0.598
|
0.675
|
0.648
|
0.705
|
0.675
|
- LR Logistic regression, RF Random forest, SVM Support vector machine, kNN k-nearest neighbor, DT Decision tree, NB Naive bayes, ANN Artificial neural network, SGD Stochastic gradient descent
