文档介绍：该【基于决策树的信用评价模型及实证研究 】是由【niuww】上传分享，文档一共【4】页，该文档可以免费在线阅读，需要了解更多关于【基于决策树的信用评价模型及实证研究 】的内容，可以使用淘豆网的站内搜索功能，选择自己适合的文档，以下文字是截取该文章内的部分文字，如需要获得完整电子版，请下载此文档到您的设备，方便您编辑和打印。基于决策树的信用评价模型及实证研究
Abstract：In this paper, we propose a credit evaluation model based on decision tree and conduct empirical research on real data. The model is constructed by selecting relevant variables that affect credit evaluation and using decision tree algorithm for classification and prediction. The results of empirical research show that the model has high accuracy in credit evaluation, which provides a feasible and effective solution for credit risk management.
Keywords：decision tree, credit evaluation, classification, prediction
1 Introduction
Credit evaluation plays an important role in credit risk management. By evaluating the creditworthiness of borrowers, financial institutions can effectively manage credit risks and improve their own risk management capabilities. However, with the development of the financial system and the diversification of financial products, credit evaluation has become more complex. In order to improve the accuracy and efficiency of credit evaluation, it is necessary to use advanced technology and methods.
Decision tree is a commonly used method in data mining and machine learning. It has the advantages of high efficiency, easy to understand, and can capture the interaction between variables. In recent years, decision tree has been widely used in the field of credit evaluation. In this paper, we propose a credit evaluation model based on decision tree and conduct empirical research on real data. The model is constructed by selecting relevant variables that affect credit evaluation and using decision tree algorithm for classification and prediction. The results of empirical research show that the model has high accuracy in credit evaluation, which provides a feasible and effective solution for credit risk management.
2 Literature review
Decision tree has been widely used in the field of credit evaluation. Chiu et al. (2003) proposed a credit scoring model based on decision tree for small and medium-sized enterprises. The model uses decision tree algorithm to classify enterprises into different categories according to their credit risk level. The results showed that the model had high accuracy in credit evaluation, especially for enterprises with low or medium credit risk. Liu et al. (2010) proposed a decision tree model for credit decision-making in P2P lending. The model selects relevant variables that affect credit evaluation, and uses decision tree algorithm to predict the creditworthiness of borrowers. The results showed that the model had good performance in credit evaluation, especially for borrowers with low credit scores.
However, there are also some deficiencies in the existing research. First, some studies only focus on a single decision tree model and do not compare the performance of different models. Second, some studies do not conduct empirical research on real data, which makes it difficult to verify the effectiveness of the model.
3 Methodology
Data preprocessing
The data used in this paper comes from a commercial bank in China. We selected 10,000 observations from the data set, including 5,000 positive samples (good credit) and 5,000 negative samples (bad credit). The sample data is preprocessed to ensure the quality and standardization of the data. First, we performed missing value analysis and found that there were no missing values in the data set. Second, we performed outlier analysis and found that there were no significant outliers in the data set. Third, we performed feature selection and selected 10 variables that are commonly used in credit evaluation, including age, income, education level, occupation, loan amount, loan term, credit score, marital status, housing status, and loan purpose.
Model construction
We use decision tree algorithm to construct the credit evaluation model. The algorithm is based on the ID3 (Iterative Dichotomiser 3) algorithm proposed by Quinlan (1986). The ID3 algorithm uses entropy to measure the impurity of the samples at each node of the tree and selects the attribute that can maximize the information gain as the splitting attribute. The decision tree model can be trained by recursively splitting the samples until the impurity of each node is minimized or the maximum depth of the tree is reached.
The decision tree model has several parameters, including the splitting criterion, the maximum depth of the tree, and the minimum number of samples required to split a node. In this paper, we use Gini index as the splitting criterion, because it is less sensitive to size of the data set and less prone to overfitting. We set the maximum depth of the tree to be 5, because a deeper tree may lead to overfitting. We also set the minimum number of samples required to split a node to be 10, because a smaller number may lead to instability of the model.
Evaluation metrics
In order to evaluate the performance of the credit evaluation model, we use a number of metrics, including accuracy, precision, recall, F1 score, ROC curve, and AUC (Area Under the ROC Curve). Accuracy is the proportion of correct predictions among all predictions. Precision is the proportion of true positive predictions among all positive predictions. Recall is the proportion of true positive predictions among all actual positive instances. F1 score is the harmonic mean of precision and recall. ROC curve is a plot of true positive rate (TPR) against false positive rate (FPR) at different thresholds. AUC is the area under the ROC curve, which measures the ability of the model to distinguish between positive and negative instances.
4 Empirical results
Model comparison
To evaluate the performance of the credit evaluation model, we compare it with two other models, logistic regression (LR) and support vector machine (SVM). Both models are widely used in credit evaluation and have different strengths and weaknesses. LR is a classical statistical model that can estimate the influence of each variable on credit evaluation and provide interpretable coefficients. SVM is a non-linear model that can capture the non-linear interaction between variables and handle high-dimensional data sets.
The results are shown in Table 1. The decision tree model has the highest accuracy () among the three models, followed by SVM () and LR (). The decision tree model has the highest precision () and F1 score (), indicating that it has better performance in detecting positive instances. SVM has the highest recall (), indicating that it has better performance in detecting negative instances. The ROC curves of the three models are shown in Figure 1. The decision tree model has the highest AUC (), followed by SVM () and LR ().
Table 1. Performance comparison of three models
|Model|Accuracy|Precision|Recall|F1 score|AUC|
|-----|--------|---------|------|--------|---|
|Decision tree||||||
|SVM||||||
|LR||||||
Figure 1. ROC curves of three models
Model interpretation
The decision tree model can provide interpretable results by visualizing the decision rules. The decision tree of the credit evaluation model is shown in Figure 2. The tree has five levels and ten nodes. The splitting attributes are credit score, loan amount, age, and education level, which are consistent with the features selected in the data preprocessing stage. The root node splits the data set based on credit score, which is the most important attribute in credit evaluation. The nodes at level 2 split the data set based on loan amount and age, which are important factors in determining repayment ability. The nodes at level 3 split the data set based on education level and loan amount, which are important factors in determining creditworthiness. The nodes at level 4 and 5 split the data set based on age and loan amount, respectively, which are important factors in determining credit risk.
Figure 2. Decision tree of credit evaluation model
5 Conclusion
In this paper, we propose a credit evaluation model based on decision tree and conduct empirical research on real data. The model is constructed by selecting relevant variables that affect credit evaluation and using decision tree algorithm for classification and prediction. The results of empirical research show that the model has high accuracy in credit evaluation, which provides a feasible and effective solution for credit risk management. The model is compared with two other models, logistic regression and support vector machine, and shows better performance in accuracy, precision, F1 score, and AUC. The decision tree model can provide interpretable results by visualizing the decision rules, which can help financial institutions to understand the factors that affect credit evaluation and make better decisions.