## 4 Experimental Results
### Evaluation of the Model with XAI Methods
#### 4.3.1 Explaining the Best Model with LIME  
LIME is a method that enables the examination of the prediction probabilities for the relevant class value of the selected examples from the dataset, along with the features and values used to make this prediction. In this study, random examples belonging to 10 classes that were correctly predicted were selected, and the LIME outputs shown in Figure 4 were included. These outputs provide insight into the decision-making process of the model while making predictions for the selected sample. The LIME results consist of three parts: (1) model predictions for the relevant example, (2) contributions of the features, and (3) actual values of the features. In Figure 4, a sample was selected for each CS, and the corresponding LIME outputs were examined. Figure 4: _Cont._  
According to Figure 4a, the model predicted \"1\" for the selected sample at a rate of 100%. The graph in the second area summarizes the decision status according to the values in the selected sample while the model predicted \"1\". The features listed under the \"NOT 1\" heading in green are the features indicating that the model is not \"1\". The features listed under the \"1\" heading in blue are the features contributing to the probability of the model being \"1\" while making the prediction. When examining the model, the five most important features identified are: cash payment percentage, late payment rate, gender, number of invoice types, and number of paid invoices. According to the model, a cash payment percentage of less than or equal to 0.50, female gender, the number of paid invoices being 10, and the number of paid invoices being 10-13 played the most important roles in the prediction. A late payment rate of 0.08 and 0.40, along with a number of invoice types greater than 4 are the features contributing to the model prediction not being \"1\". The third column shows the real values of the features in the selected sample. Figure 4: LIME Outputs for Randomly Selected Samples for all Credit Scores. The estimate for the sample whose LIME output is given in Figure 4b is \"2\" at 100%. The most significant features contributing to this prediction are a cash payment percentage between 0.50 and 0.80, a late payment rate greater than 0.71, male gender, an average number of days paid late greater than 12.33, and an age range from 43 to 51. The average number of days paid early and the number of invoices paid have a negative effect on the estimate. According to the output in Figure 4c, the estimate is definitely \"3\". The most important features contributing to this prediction include a late payment rate greater than 0.71, an age of less than 43, an average number of days paid early of less than 1.5, and male gender. The cash payment percentage and total payment amount features contributed to the fact that the model estimate is not \"3\". When Figure 4d is examined, it is seen that the estimate is \"4\". The most important features used to make this estimate are highlighted in orange. The three key features are cash payment percentage being less than or equal to 0.80, the average number of days paid early being less than 1.5, and male gender. The overdue payment rate, average number of days paid late, and total payment amount are the features that the model does not use when making the \"4\" decision. The selected sample in Figure 4e belongs to class \"5\" with a rate of 86%, followed by class \"6\" with a rate of 8%, class \"1\" with a rate of 3%, and class \"4\" with a rate of 2%. When estimating for class \"5\", the most important features are a cash payment percentage between 0.8 and 1, a late payment rate greater than 0.71, an average number of days paid early less than 1.50, and male gender. The average number of days paid late, the number of invoices paid, the total payment amount, and the age features contribute to the probability that the selected sample belongs to the other classes. In Figure 4f, the model's prediction is \"6\" at a rate of 91%.