## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
However, even for experts, achieving high-performing models is resource-intensive ([PERSON] et al. [31]). Therefore, in order to mitigate such costs with the development of machine learning, a novel concept has arisen. Automated machine learning (AutoML) is a newly emerged field that combines machine learning techniques with automation techniques. It provides a promising solution for building machine learning models without the need for extensive human intervention. It has been the subject of comprehensive research and development ([PERSON] et al. [63]). The increasing processing capability has led to the proliferation of AutoML, which has attracted substantial attention from both industry and academia ([PERSON] et al. [64]). In general, it has four types. The first one is hyperparameter tuning. It can select the ideal model hyperparameter. The second one is a combined algorithm and hyperparameter search (CASH) that chooses an estimator and hyperparameters. The third type is the neural architecture search (NAS) which is able to choose a deep learning architecture. Last, but not least, is meta-learning or learning to learning, i.e., picking a better model on a new dataset leveraging knowledge gained from a corpus of datasets ([PERSON] et al. [65]). The workflow of AutoML on the H2O platform is shown in Figure 4 below. A Framework for AutoML

In this study, we implement AutoML on the H2O platform. H2O is a machine learning and predictive analytics platform which is fast, scalable, and distributed. It facilitates the development of machine learning models on large datasets. The fundamental programming code of H2O is coded in Java. Due to its developed algorithms such as deep learning and tree ensembles, H2O offers an inviting tool for big data science and provides fast speed, ease of use, and high-quality model deployment (=[PERSON] et al. [31]). This package automates the training of numerous candidate models and stacked ensembles through a single function ([PERSON] et al. [66]). It provides a diverse range of algorithms, including gradient boosting machines (GBMs), random forests, extreme gradient boosting (XGBoost), StackedEnsemble, and generalized linear models (GLMs), which enhances the diversity of candidate models ([PERSON] et al. [34]). The two most important operations of H2O in this experiment are data manipulation and algorithms. In the data manipulation, we first need to upload and import files. After that, we need to convert the type of label into a factor. The third step is to slice columns into two divisions: 75% training samples and 25% test samples. Finally, we use the AutoML algorithm to train the datasets. H2O will automatically select models for training ranked based on model accuracy. A portion of models adopted by H2O is shown below:

Figure 4: Illustration of the Automated machine learning (AutoML) workflow on the H2O platform. 1. GBM\(-\)grid\(-\)grid\(-\)1\(-\)AutoML\(-\)4\(-\)20230314\(-\)214420\(-\)model\(-\)1;
2. StackedEnsemble\(-\)AllModels\(-\)1\(-\)AutoML\(-\)4\(-\)20230314\(-\)214420;
3. StackedEnsemble\(-\)BestOffFamily\(-\)1\(-\)AutoML\(-\)4\(-\)20230314\(-\)214420;
4. GBM\(-\)5\(-\)AutoML\(-\)4\(-\)20230314\(-\)214420. The workflow of the AutoML on the H\({}_{2}\)O platform in our experiments is shown in Figure 4. Before employing AutoML, manually preprocessing the data is required, which includes missing value filling, outliers handling, one-hot coding, etc. Based on our settings, the H\({}_{2}\)O AutoML framework divides the dataset into training and test sets. This framework then executes processes including the evaluation criteria selection, feature selection, machine learning model selection, hyperparameter tuning, etc., respectively. Upon completing the model training, H\({}_{2}\)O will select the best performing model and predict the test sets according to the preset evaluation metrics.