## 2 Materials and Methods
### Interpretability and Feature Importance  
#### 2.7.1 Model Interpretability  
Machine learning models are trained to provide plenty of opportunities to enhance our public and private lives. However, this can raise a challenge: How do we understand the decisions suggested by models so we can trust them? In fact, it can be challenging to comprehend a model's internal workings when one concentrates on data-driven methodologies, particularly machine learning and pattern recognition models. At the very least, interpretability can make it easier to grasp different components of a model, providing insights that different stakeholders can use (see [PERSON] et al. [58]). It is tough to describe interpretability as models might only be simple to understand in certain aspects, and many alleged interpretable models are usually problem-specific. The machine learning community has no common platforms for explaining models' interpretable properties. The task of interpretation appears underspecified. [PERSON] et al. [59] endeavored to enhance the discourse on interpretability. They initially explored the underlying motivations of interest in interpretability, revealing that they are divergent and in conflict with each other. They also examined the viability and desirability of various notions. However, differently to measures of performance like accuracy, evaluating interpretability is not an easy job, because there is little agreement on what interpretability is, as well as how to measure it for benchmarking in machine learning, so a course for the definition and critical evaluation of interpretability was been outlined by [PERSON] et al. [60]. .7.2 Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP)  
The machine learning training process and optimization is frequently based on accuracy metrics. Precisely estimating the costs of a model is a difficult task in many circumstances. When metrics used to evaluate a model are inadequate, interpretability becomes necessary. Interpretability aids in comprehending a model's learning process. There are three main things we looking for from interpretability, i.e., correctness, transferability, and causality (see [PERSON] et al. [58]). When we obtain training results, do we trust them? Also, do we have confidence in the relationship learned from the model? All of these refer to the correctness of the model and how the prediction model is applied from one application domain to another, indicating the transferability. Finally, causality denotes the situation that the machine learning model learns the relationship between the input variables and the output results, which is crucial because the model's predictions are based upon this relationship. Model interpretability is mainly divided into two divisions: transparency and post hoc explanations (see [PERSON] et al. [58]). Transparency signifies the ability of humans to comprehend the internal mechanisms or inner workings of the model, while post hoc explanations are techniques that enable the visualization of what has been learned by models. The two most popular post hoc techniques are LIME ([PERSON] et al. [38]) and SHAP ([PERSON] et al. [39]). LIME is an innovative method of explanation that can explain predictions of any classifier in a comprehensible and trustworthy way ([PERSON] et al. [38]). In the explainability approaches, LIME belongs to the category of local explanations which is model-agnostic ([PERSON] et al. [58]). It explains individual predictions by fitting the trained model on the test datasets. Another technique is SHAP, which is the game-theory-inspired feature relevance explanation. Like the LIME, SHAP can also be applicable to all model types. Each feature can be assigned an importance value called the SHAP value for prediction. SHAP values are originally from the widely used cooperative game theory. They represent the contribution of each feature to the final classification result.