## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
This was reached by picking the features intensity.4.PR, intensity.2.PR, Tenure, and intensity.5.PR. The AutoML model used was DeepLearning-1-AutoML. As can be seen from Table 11, it is evident that the combination of the method XAI and AutoML achieves higher accuracy. Apparently, this finding highlights the performance of the XAI and AutoML approaches. Among the projects from the global manufacturing companies, projects D and F exhibited the largest feature selection count, i.e., four features, which resulted in a precision of 83.83% and 91.41%. It was also the best accuracy achieved. Projects B and E exhibit the smallest feature selection count, with only two important features selected, resulting in an accuracy of 88.94% and 82.34%. Project A achieved an accuracy of 84.16% with the three most significant features being selected. The test results highlight the capacity of XAI to accurately identify important features that can be classified using the AutoML method with high accuracy. To improve the robustness and generalization of the model, we employed L2 regularization for statistical machine learning models, AutoML, and GCN. We used 10-fold cross-validation in statistical machine learning models and AutoML. However, it is difficult to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Project A** & **Project B** & **Project C** & **Project D** & **Project E** & **Project F** \\ \hline Number of the most important variables & 3 & 2 & 3 & 4 & 2 & 4 \\ \hline Accuracy & 0.8416 & 0.8894 & 0.8818 & 0.8383 & 0.8234 & 0.9141 \\ \hline Project A selects Id, CL English and Grado. Project B selects the Id and Modularity Class. Project C intensity.4.PR, intensity.5.PR, and intensity.1.PR. Project D selects the Id, intensity.4.PR, intensity.1.PR, and intensity.3.PR. Project E selects intensity.5.PR and ID. Project F selects intensity.4.PR, intensity.2.PR, Tenure and intensity.5.PR. & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Model accuracy by Automated Machine Learning (AutoML) after important features were chosen by Explainable Artificial Intelligence (XAI). Figure 7: The workflow of the explainable artificial intelligence (XAI) method SHapley Additive exPlanations (SHAP). apply 10-fold cross-validation in graph convolutional networks (GCNs). Algorithm 2, proposed by [PERSON] et al. [77], conducts model selection based on a fixed train-validation-test split. As such, there is no \"cross\"-validation even with the minimum of two folds. [PERSON] et al. [78] proposed that the embedded weights continue to display obvious color divergence when the number of layers is set to 1, while switching the allocation method to cross-validation. Therefore, for GCN, we divided the datasets into 75% for the training set, 15% for the validation set, and 15% for the test set. We repeat 10 experiments with different randomization for GCN. This partition ensures that the model has sufficient data for training, facilitating hyperparameter tuning on the validation set, and allows for an accurate evaluation of the model's performance on the test set. In machine learning, techniques like L2 regularization and cross-validation play a crucial role in improving model accuracy and performance ([PERSON] et al. [79]). L2 regularization, also known as ridge regularization, works by applying a penalty to large coefficients, which reduces model complexity and avoids overfitting. This regularization method aids in improving the model generalization, ensuring good performance on new data by balancing bias-variance tradeoff ([PERSON] et al. [80]). Ten-fold cross-validation is a commonly used strategy to assess model performance. By splitting the dataset into 10 subsets and using nine for training and one for testing in each iteration, this method offers a thorough evaluation of the model's prediction. It reduces the overfitting risk and ensures validation on different subsets of the data, resulting in more reliable performance ([PERSON] et al. [81]).