# 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-Sscore** & **G-Mean** \\ \hline \multirow{8}{*}{No sampling} & Logistic & 0.788 +/ – 0.072 & 0.762 +/ – 0.062 & 0.774 +/ – 0.056 & 0.777 +/ – 0.053 \\  & Random forest & 0.788 +/ – 0.071 & 0.771 +/ – 0.070 & 0.783 +/ – 0.065 & 0.778 +/ – 0.067 \\  & AdaBoost & 0.762 +/ – – 0.038 & 0.721 +/ – 0.040 & 0.736 +/ – 0.041 & 0.737 +/ – 0.039 \\  & XGBoost & 0.778 +/ – 0.059 & 0.762 +/ – 0.059 & 0.775 +/ – 0.051 & 0.774 +/ – 0.053 \\  & Neural Network & 0.791 +/ – 0.069 & 0.759 +/ – 0.061 & 0.771 +/ – 0.054 & 0.775 +/ – 0.053 \\  & LightGBM & 0.766 +/ – 0.022 & 0.764 +/ – 0.019 & 0.777 +/ – 0.018 & 0.773 +/ – 0.023 \\  & CatBoost & 0.783 +/ – 0.018 & 0.771 +/ – 0.028 & 0.783 +/ – 0.023 & 0.775 +/ – 0.023 \\  & TabNet & 0.653 +/ – 0.022 & 0.678 +/ – 0.018 & 0.695 +/ – 0.020 & 0.685 +/ – 0.016 \\ \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 & 0.763 +/ – 0.041 & 0.758 +/ – 0.043 & 0.771 +/ – 0.041 & 0.764 +/ – 0.042 \\  & CatBoost & 0.775 +/ – 0.039 & 0.759 +/ – 0.058 & 0.772 +/ – 0.054 & 0.770 +/ – 0.053 \\  & TabNet & 0.717 +/ – 0.039 & 0.720 +/ – 0.043 & 0.735 +/ – 0.037 & 0.727 +/ – 0.038 \\ \hline \multirow{8}{*}{ADASYN} & Logistic & 0.794 +/ – 0.067 & 0.788 +/ – 0.055 & 0.799 +/ – 0.051 & 0.797 +/ – 0.054 \\  & Random forest & 0.793 +/ – 0.070 & 0.773 +/ – 0.058 & 0.785 +/ – 0.050 & 0.783 +/ – 0.055 \\  & AdaBoost & 0.715 +/ – 0.042 & 0.703 +/ – 0.048 & 0.719 +/ – 0.045 & 0.716 +/ – 0.043 \\  & XGBoost & 0.765 +/ – 0.075 & 0.764 +/ – 0.058 & 0.776 +/ – 0.052 & 0.772 +/ – 0.054 \\  & Neural Network & 0.796 +/ – 0.064 & **0.792 +/ – 0.059** & **0.802 +/ – 0.056** & **0.800 +/ – 0.056** \\  & LightGBM & 0.758 +/ – 0.024 & 0.742 +/ – 0.038 & 0.756 +/ – 0.036 & 0.749 +/ – 0.037 \\  & CatBoost & 0.780 +/ – 0.038 & 0.774 +/ – 0.038 & 0.786 +/ – 0.036 & 0.778 +/ – 0.037 \\  & TabNet & 0.709 +/ – 0.075 & 0.711 +/ – 0.077 & 0.726 +/ – 0.072 & 0.720 +/ – 0.073 \\ \hline \multirow{8}{*}{ROS} & Logistic & 0.786 +/ – 0.071 & 0.766 +/ – 0.071 & 0.778 +/ – 0.067 & 0.780 +/ – 0.065 \\  & Random forest & 0.797 +/ – 0.068 & 0.764 +/ – 0.084 & 0.777 +/ – 0.077 & 0.779 +/ – 0.077 \\  & AdaBoost & 0.710 +/ – 0.050 & 0.688 +/ – 0.052 & 0.704 +/ – 0.052 & 0.710 +/ – 0.045 \\  & XGBoost & 0.769 +/ – 0.068 & 0.748 +/ – – 0.078 & 0.761 +/ – 0.070 & 0.764 +/ – 0.065 \\  & Neural Network & 0.788 +/ – 0.067 & 0.749 +/ – – 0.047 & 0.761 +/ – – 0.043 & 0.761 +/ – 0.043 \\  & LightGBM & 0.793 +/ – 0.014 & 0.767 +/ – – 0.013 & 0.767 +/ – 0.013 & 0.767 +/ – 0.013 \\  & CatBoost & 0.794 +/ – – 0.013 & 0.767 +/ – – 0.010 & 0.766 +/ – 0.010 \\  & TabNet & 0.780 +/ – – 0.014 & 0.760 +/ – – 0.014 & 0.760 +/ – 0.015 & 0.759 +/ – 0.014 \\ \hline \multicolumn{2}{c}{PIA-Soft (Ours)} & **0.798 +/ – 0.045** & 0.781 +/ – 0.051 & 0.795 +/ – 0.047 & 0.795 +/ – 0.049 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The prediction performance for German dataset over different evaluation metrics.