## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
Compared with LIME, SHAP's innovative elements consist of the discovery of a new class of feature importance measurements ([PERSON] et al. [39]). Apart from explaining a single feature, SHAP is also capable of explaining both a single feature and the entire dataset. This is the reason we choose SHAP instead of LIME in our experiment. By explaining the entire dataset, the significance of each feature and their influence on final classification results can be ranked based upon their importance. The importance of the features and their impact on the prediction by SHAP are shown in Figure 3. Figure 3 shows the summary charts of the real-world project A from an IT company as well as project E and project F from an international manufacturing company. Two of the widely used SHAP methods are Kernel SHAP ([PERSON] et al. [39]) and Tree SHAP ([PERSON] et al. [61]). Kernel SHAP is a model-agnostic method that employs a weighted linear regression model to quantify each feature's contributions toward predictions, though at a high computational cost. On the other hand, Tree SHAP is specifically designed for tree-based models, such as decision trees, random forest, and gradient boosting. It offers an efficient and precise approach, avoiding the local inaccuracies and global inconsistencies present in other tree-based interpretability techniques. Given Tree SHAP's efficiency and precision, we selected the XGBoost model as the trained model in the feature importance analysis experiments and used Tree SHAP to interpret the model's predictions. It should be emphasized that we use the XGBoost model for feature analysis, and then the selected features are used as inputs for training the GCN. Therefore, we do not interpret the GCN in our paper. More details about the experimental part of XAI are given in the experimental section. The concept of Tree SHAP is shown in Algorithm 2 below. Tree SHAP starts with the training of a predictive XGBoost model \(f(x)\) on dataset \(X\). It then computes the SHAP value \(\phi_{j}\) for each feature using the algorithm presented by [PERSON] et al. [62]. Once the SHAP values are obtained, the algorithm proceeds to construct an explanation model, denotedas \(g(z^{\prime})\), incorporating the computed SHAP values. In this algorithm, the function \(g(z^{\prime})\) is defined as the sum of the product of each SHAP value \(\phi_{j}\) and \(z^{\prime}_{j}\) in addition to a base value \(\phi_{0}\). The variable \(z^{\prime}\) belongs to a \(d\)-dimensional space where each element can be either 0 or 1. This explanation model's aim is to quantify and convey the feature importance for each data point in the dataset \(X\), making up the primary output of this algorithm. These findings illustrate how each feature impacts the XGBoost model's prediction for each data point. ```
0: Dataset \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) where each \(x_{i}\in\mathbb{R}^{d}\)
0: Feature importance of each feature for each data point in \(X\)
1: Train predictive XGBoost model \(f(x)\) on \(X\)
2:for each feature \(j=\{1,2,...,d\}\)do
3: Calculate SHAP value \(\phi_{j}\) for each feature based on Algorithm 2 presented in [62]
4:end
5: Construct an explanation model \(g(z^{\prime})=\phi_{0}+\sum_{j=1}^{d}\phi_{j}z^{\prime}_{j}\), where \(z^{\prime}\in\{0,1\}^{d}\) (Equation (1) in [39; 62])
6:return the explanation model \(g(z^{\prime})\) which represents the feature importance for each data point in \(X\)

Figure 3: Importance and impact of features on prediction by SHAP. Summary chart (**a**) explains the entire dataset in Project A, chart (**b**) explains the entire dataset in Project E, and chart (**c**) explains the entire dataset in Project F.

### Automated Machine Learning (AutoML)

In recent years, the development of a reliable machine learning (ML) system for a particular task has been heavily dependent on human experience, thereby impeding its widespread adoption. Generally speaking, the development of machine learning models requires notable effort from experts who manually design models.