## 4 Experimental Results
### Evaluation of the Model with XAI Methods
#### 4.3.1 Explaining the Best Model with LIME
Based on the features in this example, the prediction belongs to class \"4\" at a rate of 3% and classes \"1\", \"5\", or other at a rate of 2%. In the sample shown in Figure 4g, the model predicts class \"7\" with 100% confidence. The most important features contributing to this prediction include cash payment percentage in the range of 0.80 to 1, an average early payment day in the range 1.50-5, a late payment rate less than or equal to 0.08, the number of invoice types being less than 2, and a total payment amount ranging from 1582.25-2866.92. The features and values given under the heading \"NOT 7\" are used for estimating classes other than \"7\". In Figure 4h, the model predicted \"8\" with a probability of 71%. The selected sample could belong to \"9\" with a probability of 10%, class \"7\" with a probability of 8%, class \"10\" with a probability of 5%, and other classes with a probability of 6%. The features that enable the model to predict \"8\" include a late payment rate less than 0.08, an average early payment period between 5-8 days, a total payment amount greater than 2866.92, the bill type electricity, and male gender. In Figure 4i, the model made a prediction for class \"9\" with 100% probability. The most important features influencing this prediction include the late payment rate, average number of days paid early, cash payment percentage, gender, average number of days paid late, and number of invoice types. In contrast, the invoice type internet and an age range from 43 to 51 did not contribute to the prediction. For the example in Figure 4j, the model made an estimate of \"10\". The most important features contributing to this prediction are the average number of days paid early, late payment rate, total payment amount, and gender. The invoice type, GSM, was not used in this estimate. When drawing inferences about explainability and interpretability via LIME, more reliable explanations and interpretations can be achieved by increasing the variety of selected samples and labels. Efficient outputs can be obtained by abstracting the end user from the complex machine learning model used. Explaining the Best Model with SHAP  
The SHAP approach offers a more detailed overview of the impact of each feature on a specific label. The feature importance can be examined for each label value. In our study, there are 10 label values ranging from 1 to 10, where 1 represents the worst credit score and 10 the best. Feature importance graphs for each label are prepared and displayed in Figure 5.  
The details of SHAP outputs in Figure 5 are summarized as follows:  
* The \(y\)-axis represents the features used in the model training. The features that have the most impact on the result are listed from top to bottom in order of their impact on the result, with the most significant features at the top. * Each point on the \(X\)-axis represents a calculated Shapley value. Positive values on the \(X\)-axis are values that contribute positively to the corresponding label value estimate, while negative values indicate a negative contribution to the estimate. * The colors of the points on the \(X\)-axis help to interpret the numerical value of the data. As the color transitions from blue to red, it signifies an increase in the numerical value. * The two most important features in estimating customers with a credit score between 1-8 are cash payment percentage and late payment rate. It is evident that as the credit score value increases, so does the cash payment percentage. When looking at customers in this range, it can be concluded that those who pay their bills late have lower credit scores. * Examining the graphs for customers with credit scores of 9 and 10 reveals that the late payment rate is the most important feature. It can be said that the people with the highest scores do not have a habit of incurring overdue payments. In addition, these people tend to pay their bills before the due date and mostly make their payments in cash.