# A Partially Interpretable Adaptive Softmax Regression for Credit Scoring
## 4 Experimental Results
### Machine Learning Baselines and Hyperparameter Setting
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sampling Method** & **Model** & **AUC** & **Accuracy** & **F-Score** & **G-Mean** \\ \hline \multirow{8}{*}{No sampling} & Logistic & \(0.911+/-0.053\) & \(0.869+/-0.047\) & \(0.868+/-0.047\) & \(0.862+/-0.046\) \\  & Random forest & \(0.916+/-0.064\) & \(0.883+/-0.053\) & \(0.883+/-0.052\) & \(0.876+/-0.052\) \\  & AdaBoost & \(0.928+/-0.035\) & \(0.894+/-0.024\) & \(0.894+/-0.023\) & \(0.891+/-0.024\) \\  & XGBoost & \(0.915+/-0.059\) & \(0.870+/-0.067\) & \(0.870+/-0.068\) & \(0.868+/-0.065\) \\  & Neural Network & \(0.904+/-0.052\) & \(0.867+/-0.051\) & \(0.866+/-0.051\) & \(0.860+/-0.049\) \\  & LightGBM & \(0.937+/-0.022\) & \(0.904+/-0.021\) & \(0.904+/-0.021\) & \(0.902+/-0.022\) \\  & CatBoost & \(\mathbf{0.938+/-0.015}\) & \(0.910+/-0.018\) & \(0.910+/-0.018\) & \(0.907+/-0.017\) \\  & TabNet & \(0.852+/-0.047\) & \(0.823+/-0.034\) & \(0.822+/-0.034\) & \(0.816+/-0.038\) \\ \hline \multirow{8}{*}{SMOTE} & Logistic & \(0.910+/-0.054\) & \(0.873+/-0.056\) & \(0.873+/-0.056\) & \(0.867+/-0.056\) \\  & Random forest & \(0.916+/-0.065\) & \(0.884+/-0.056\) & \(0.884+/-0.056\) & \(0.882+/-0.055\) \\  & AdaBoost & \(0.923+/-0.039\) & \(0.879+/-0.045\) & \(0.879+/-0.045\) & \(0.876+/-0.044\) \\  & XGBoost & \(0.903+/-0.058\) & \(0.855+/-0.060\) & \(0.854+/-0.061\) & \(0.848+/-0.060\) \\  & Neural Network & \(0.906+/-0.054\) & \(0.842+/-0.109\) & \(0.826+/-0.155\) & \(0.834+/-0.117\) \\  & LightGBM & \(0.936+/-0.025\) & \(0.898+/-0.023\) & \(0.898+/-0.023\) & \(0.897+/-0.023\) \\  & CatBoost & \(0.931+/-0.019\) & \(\mathbf{0.914+/-0.019}\) & \(\mathbf{0.914+/-0.019}\) & \(\mathbf{0.912+/-0.018}\) \\  & TabNet & \(0.836+/-0.023\) & \(0.821+/-0.030\) & \(0.822+/-0.031\) & \(0.820+/-0.031\) \\ \hline \multirow{8}{*}{ADASYN} & Logistic & \(0.911+/-0.053\) & \(0.876+/-0.051\) & \(0.876+/-0.051\) & \(0.870+/-0.050\) \\  & Random forest & \(0.917+/-0.065\) & \(0.880+/-0.055\) & \(0.880+/-0.054\) & \(0.875+/-0.054\) \\  & AdaBoost & \(0.916+/-0.039\) & \(0.873+/-0.039\) & \(0.873+/-0.039\) & \(0.871+/-0.038\) \\  & XGBoost & \(0.917+/-0.060\) & \(0.851+/-0.103\) & \(0.835+/-0.147\) & \(0.841+/-0.122\) \\  & Neural Network & \(0.904+/-0.054\) & \(0.863+/-0.046\) & \(0.863+/-0.046\) & \(0.859+/-0.045\) \\  & LightGBM & \(0.934+/-0.023\) & \(0.898+/-0.015\) & \(0.898+/-0.015\) & \(0.896+/-0.016\) \\  & CatBoost & \(0.934+/-0.018\) & \(0.904+/-0.016\) & \(0.904+/-0.016\) & \(0.901+/-0.015\) \\  & TabNet & \(0.800+/-0.063\) & \(0.804+/-0.058\) & \(0.804+/-0.058\) & \(0.801+/-0.057\) \\ \hline \multirow{8}{*}{ROS} & Logistic & \(0.911+/-0.053\) & \(0.879+/-0.052\) & \(0.878+/-0.052\) & \(0.872+/-0.052\) \\  & Random forest & \(0.917+/-0.065\) & \(0.883+/-0.055\) & \(0.883+/-0.055\) & \(0.878+/-0.055\) \\  & AdaBoost & \(0.912+/-0.045\) & \(0.862+/-0.062\) & \(0.861+/-0.063\) & \(0.859+/-0.061\) \\  & XGBoost & \(0.909+/-0.067\) & \(0.857+/-0.052\) & \(0.855+/-0.052\) & \(0.849+/-0.052\) \\  & Neural Network & \(0.903+/-0.055\) & \(0.846+/-0.096\) & \(0.833+/-0.132\) & \(0.835+/-0.117\) \\  & LightGBM & \(0.926+/-0.026\) & \(0.892+/-0.024\) & \(0.892+/-0.024\) & \(0.891+/-0.024\) \\  & CatBoost & \(0.924+/-0.012\) & \(0.902+/-0.018\) & \(0.902+/-0.019\) & \(0.900+/-0.018\) \\  & TabNet & \(0.842+/-0.048\) & \(0.802+/-0.059\) & \(0.803+/-0.058\) & \(0.802+/-0.059\) \\ \hline \multicolumn{2}{c}{PIA-Soft (Ours)} & \(0.934+/-0.041\) & \(0.896+/-0.079\) & \(0.894+/-0.086\) & \(0.895+/-0.075\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The prediction performance for Australia dataset over different evaluation metrics.