# Research on PPP Enterprise Credit Dynamic Prediction Model
## 5 Conclusions and Future Work  
### Conclusions  
This paper focuses on the research of the local government financing vehicles credit prediction model and analyzes the importance of multiple variables in the model output. On this basis, we established an enterprise credit evaluation XGBoost-logistic model. Combined with the credit evaluation problem of LGFVs and the current situation of the public-private partnership industry, the classification performance and interpretability process of the model were verified. The main conclusions are as follows:  
1. This paper proposes an innovative research method. The credit evaluation index of traditional LGFVs has been restructured. In view of the particularity of the PPP industry, the regional economy is introduced as an indicator. By judging the actual controller of the company and screening the LGFVs that meet the standards, it provides more accurate and more reliable basic data for the construction of the LGFVs prediction model.
2. The key indicators affecting the model output were analyzed in depth. Network, total assets, operating income, and return on equity are the variables that have the greatest impact on the model evaluation results. Moreover, asset-liability ratio and tax revenue are also important potential risk factors in LGFVs' credit evaluation. The banks and governments need to pay extra attention to these indexes in the future.
3. The XGBoost-logistic model shows more intuitive interpretability and stronger application performance. First, this paper proves that the XGBoost-logistic credit evaluation model is significantly superior to the traditional logistic regression model in terms of model distinguishing ability, sorting ability, accuracy, and stability. Second, we also explain how the characteristic variables affect the output of the model by SHAP value, and also apply dtreeviz to portray the classification decision process of the model for LGFVs with different characteristics. Finally, this paper provides an effective and scientific enterprise credit evaluation method for financial institutions and government departments, which can more accurately describe the credit risk status of LGFVs and greatly enhance the interpretability of the model. According to the important indicators that affect the LGFVS credit default behavior, it can provide targeted risk prevention countermeasures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Type** & **Coefficient** & **Logistic** & **XGBoost-Logistic** \\ \hline \multirow{6}{*}{Accuracy of training set} & \multirow{4}{*}{Default} & Average value & 61.008 & 83.648 \\  & & Minimum & 59.988 & 83.497 \\  & & Maximum & 62.787 & 83.813 \\  & & Standard deviation & 0.002 & 0.000007 \\ \cline{2-5}  & \multirow{4}{*}{Non-default} & Average value & 62.189 & 83.365 \\  & & Minimum & 61.259 & 82.437 \\  & & Maximum & 63.673 & 83.836 \\  & & Standard deviation & 0.008 & 0.005 \\ \cline{2-5}  & \multirow{2}{*}{Total} & Average value & 61.563 & 83.156 \\ \hline \multirow{6}{*}{Accuracy of test set} & \multirow{4}{*}{Default} & Average value & 62.614 & 82.107 \\  & & Minimum & 61.018 & 81.729 \\ \cline{1-1}  & & Maximum & 63.452 & 82.46 \\ \cline{1-1}  & & Standard deviation & 0.013 & 0.0009 \\ \cline{1-1} \cline{2-5}  & \multirow{4}{*}{Non-default} & Average value & 52.971 & 83.286 \\ \cline{1-1}  & & Minimum & 52.167 & 82.532 \\ \cline{1-1}  & & Maximum & 53.875 & 83.627 \\ \cline{1-1}  & & Standard deviation & 0.009 & 0.003 \\ \hline Total & \multicolumn{1}{c}{Average value} & 57.792 & 82.688 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Datasets comparison and model validation.