# A Partially Interpretable Adaptive Softmax Regression for Credit Scoring
## 4 Experimental Results
### Model Interpretability
\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 & 0.798 +/\(-\) 0.015 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 \\  & Random forest & 0.774 +/\(-\) 0.030 & 0.755 +/\(-\) 0.016 & 0.757 +/\(-\) 0.014 & 0.755 +/\(-\) 0.016 \\  & AdaBoost & 0.773 +/\(-\) 0.016 & 0.755 +/\(-\) 0.014 & 0.755 +/\(-\) 0.014 & 0.755 +/\(-\) 0.014 \\  & XGBoost & 0.787 +/\(-\) 0.018 & 0.754 +/\(-\) 0.035 & 0.756 +/\(-\) 0.029 & 0.757 +/\(-\) 0.025 \\  & Neural Network & 0.798 +/\(-\) 0.014 & 0.756 +/\(-\) 0.035 & 0.758 +/\(-\) 0.028 & 0.760 +/\(-\) 0.025 \\  & LightGBM & 0.792 +/\(-\) 0.015 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 \\  & CatBoost & 0.795 +/\(-\) 0.014 & 0.768 +/\(-\) 0.010 & 0.768 +/\(-\) 0.010 & 0.768 +/\(-\) 0.010 \\  & TabNet & 0.782 +/\(-\) 0.013 & 0.760 +/\(-\) 0.010 & 0.760 +/\(-\) 0.010 & 0.760 +/\(-\) 0.010 \\ \hline \multirow{8}{*}{SMOTE} & Logistic & 0.798 +/\(-\) 0.015 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 \\  & Random forest & 0.776 +/\(-\) 0.025 & 0.752 +/\(-\) 0.023 & 0.754 +/\(-\) 0.018 & 0.753 +/\(-\) 0.020 \\  & AdaBoost & 0.725 +/\(-\) 0.019 & 0.715 +/\(-\) 0.021 & 0.715 +/\(-\) 0.021 & 0.715 +/\(-\) 0.020 \\  & XGBoost & 0.782 +/\(-\) 0.029 & 0.750 +/\(-\) 0.044 & 0.752 +/\(-\) 0.036 & 0.751 +/\(-\) 0.042 \\  & Neural Network & 0.795 +/\(-\) 0.020 & 0.764 +/\(-\) 0.019 & 0.765 +/\(-\) 0.014 & 0.765 +/\(-\) 0.015 \\  & LightGBM & 0.792 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 \\  & CatBoost & 0.794 +/\(-\) 0.014 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.011 \\  & TabNet & 0.786 +/\(-\) 0.016 & 0.763 +/\(-\) 0.013 & 0.763 +/\(-\) 0.013 & 0.763 +/\(-\) 0.013 \\ \hline \multirow{8}{*}{ADASYN} & Logistic & 0.798 +/\(-\) 0.015 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 \\  & Random forest & 0.773 +/\(-\) 0.033 & 0.751 +/\(-\) 0.025 & 0.753 +/\(-\) 0.020 & 0.751 +/\(-\) 0.025 \\  & AdaBoost & 0.727 +/\(-\) 0.028 & 0.718 +/\(-\) 0.018 & 0.718 +/\(-\) 0.018 & 0.718 +/\(-\) 0.018 \\  & XGBoost & 0.781 +/\(-\) 0.032 & 0.754 +/\(-\) 0.035 & 0.756 +/\(-\) 0.029 & 0.755 +/\(-\) 0.032 \\  & Neural Network & 0.795 +/\(-\) 0.021 & 0.764 +/\(-\) 0.018 & 0.766 +/\(-\) 0.013 & 0.766 +/\(-\) 0.014 \\  & LightGBM & 0.792 +/\(-\) 0.015 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 & 0.766 +/\(-\) 0.014 \\  & CatBoost & 0.795 +/\(-\) 0.014 & 0.768 +/\(-\) 0.010 & 0.768 +/\(-\) 0.010 & 0.768 +/\(-\) 0.010 \\  & TabNet & 0.783 +/\(-\) 0.016 & 0.759 +/\(-\) 0.014 & 0.759 +/\(-\) 0.014 & 0.759 +/\(-\) 0.014 \\ \hline \multirow{8}{*}{ROS} & Logistic & 0.798 +/\(-\) 0.015 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 & 0.767 +/\(-\) 0.012 \\  & Random forest & 0.781 +/\(-\) 0.018 & 0.755 +/\(-\) 0.022 & 0.757 +/\(-\) 0.018 & 0.755 +/\(-\) 0.023 \\ \cline{1-1}  & AdaBoost & 0.725 +/\(-\) 0.016 & 0.714 +/\(-\) 0.014 & 0.714 +/\(-\) 0.014 & 0.714 +/\(-\) 0.014 \\ \cline{1-1}  & XGBoost & 0.786 +/\(-\) 0.019 & 0.750 +/\(-\) 0.047 & 0.752 +/\(-\) 0.040 & 0.755 +/\(-\) 0.029 \\ \cline{1-1}  & Neural Network & 0.799 +/\(-\) 0.015 & 0.761 +/\(-\) 0.022 & 0.763 +/\(-\) 0.017 & 0.763 +/\(-\) 0.017 \\ \cline{1-1}  & LightGBM & 0.793 +/\(-\) 0.014 & 0.767 +/\(-\) 0.013 & 0.767 +/\(-\) 0.013 & 0.767 +/\(-\) 0.013 \\ \cline{1-1}  & CatBoost & 0.794 +/\(-\) 0.013 & 0.767 +/\(-\) 0.010 & 0.766 +/\(-\) 0.010 & 0.766 +/\(-\) 0.010 \\ \cline{1-1}  & TabNet & 0.780 +/\(-\) 0.014 & 0.760 +/\(-\) 0.014 & 0.760 +/\(-\) 0.015 & 0.759 +/\(-\) 0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The prediction performance for FICO dataset over different evaluation metrics. We also display how other variables affect credit score for all datasets in Figure 4 for German dataset. These estimated coefficients from the results of the PIA-Soft model are logically consistent with the real-life and logistic regression (see Figure 5).