Table 13 From [20], bracketed numbers are confidence intervals; note we do not find where Xia et al. document the significance level for the confidence intervals; here “softer” means models are trained with all available features
Softer dataset | |||
|---|---|---|---|
Model | Accuracy | AUC | H-measure |
LR-softer | 0.7516 [0.7508, 0.7523] | 0.6151 [0.6139, 0.6163] | 0.0843 [0.0827, 0.0860] |
RT-softer | 0.6952 [0.6911, 0.6996] | 0.5444 [0.5391, 0.5493] | 0.0124 [0.0095, 0.0153] |
BNN-softer | 0.7496 [0.7480, 0.7516] | 0.6120 [0.6095, 0.6151] | 0.0801 [0.0766, 0.0843] |
RF-softer | 0.7436 [0.7415, 0.7456] | 0.6043 [0.6013, 0.6073] | 0.0695 [0.0659, 0.0733] |
GBDT-softer | 0.7504 [0.7488, 0.7520] | 0.6132 [0.6107, 0.6158] | 0.0818 [0.0784.0.0853] |
XGBoost-softer | 0.7511 [0.7496, 0.7526] | 0.6143 [0.6120, 0.6167] | 0.0833 [0.0801, 0.0866] |
CatBoost-softer | 0.7523 [0.7511, 0.7535] | 0.6162 [0.6144, 0.6180] | 0.0859 [0.0834, 0.0885] |
Model | Type I rate | Type II rate | |
|---|---|---|---|
LR-softer | 0.1557 [0.1550, 0.1565] | 0.6142 [0.6123, 0.6160] | |
RT-softer | 0.2024 [0.1978, 0.2072] | 0.7087 [0.6994, 0.7198] | |
BNN-softer | 0.1569 [0.1557, 0.1580] | 0.6190 [0.6141, 0.6231] | |
RF-softer | 0.1617 [0.1599, 0.1639] | 0.6298 [0.6241, 0.6346] | |
GBDT-softer | 0.1564 [0.1554, 0.1574] | 0.6171 [0.6130, 0.6211] | |
XGBoost-softer | 0.1560 [0.1550, 0.1569] | 0.6153 [0.6115, 0.6190] | |
CatBoost-softer | 0.1552 [0.1545, 0.1560] | 0.6124 [0.6095, 0.6152] | |