AUC | Sensitivity | Specificity | |
---|---|---|---|
Adaboost | 0.9825(0.9728,0.9887,0.9976) | 0.9408(0.9091,0.9500,0.9545) | 0.9487(0.9091,0.9524,1.0000) |
Xgboost | 0.9978(0.9952,0.9976,1.0000) | 0.9453(0.9500,0.9524,0.9524) | 0.9921(1.0000,1.0000,1.0000) |
SVM | 0.9856(0.9751,0.9925,1.0000) | 0.9490(0.9130,0.9524,1.0000) | 0.9629(0.9500,0.9524,1.0000) |
RF | 0.9861(0.9786,0.9909,0.9977) | 0.9439(0.9091,0.9524,0.9545) | 0.9541(0.9444,0.9524,1.0000) |
logistic | 0.9727(0.9524,0.9796,0.9929) | 0.9319(0.9048,0.9474,0.9524) | 0.9275(0.9048,0.9444,0.9524) |
naivebayes | 0.9644(0.9500,0.9690,0.9833) | 0.9129(0.8824,0.9048,0.9474) | 0.8391(0.8000,0.8333,0.8696) |
NPV | PPV | MCC | |
Adaboost | 0.9385(0.9048,0.9524,0.9524) | 0.9473(0.9048,0.9524,1.0000) | 0.8876(0.8548,0.9045,0.9523) |
Xgboost | 0.9447(0.9524,0.9524,0.9524) | 0.9918(1.0000,1.0000,1.0000) | 0.9369(0.9069,0.9524,0.9524) |
SVM | 0.9468(0.9048,0.9524,1.0000) | 0.9623(0.9524,0.9524,1.0000) | 0.9105(0.8581,0.9065,0.9535) |
RF | 0.9417(0.9048,0.9524,0.9524) | 0.9532(0.9500,0.9524,1.0000) | 0.8964(0.8581,0.9048,0.9524) |
logistic | 0.9303(0.9048,0.9500,0.9524) | 0.9250(0.9048,0.9500,0.9524) | 0.8574(0.8095,0.8581,0.9089) |
naivebayes | 0.9176(0.9000,0.9048,0.9524) | 0.8197(0.7619,0.8095,0.8571) | 0.7445(0.6807,0.7571,0.8095) |