# Intelligent System Using Data to Support Decision-Making
## 2 Materials and Methods
In classification, it works on the principle of finding the optimal hyperplane that best separates individual classes in the data. This hyperplane is determined in such a way as to maximize the margin--that is, the distance between the closest samples of two classes (support vectors). SVM was developed by [PERSON] and [PERSON] in the 1960s within the framework of statistical learning theory [40]. In 1995, [PERSON] and [PERSON] introduced Soft-Margin SVM, which allowed the use of SVM also for nonlinear separable data [39]. Support Vector Classifier (SVC) is a specific implementation of SVM for classification. Its task is to find the optimal hyperplane (decision boundary) that best separates individual classes in the data. It is used in both binary and multiclass classification. If the data is linearly separable, SVC finds the hyperplane with the maximum margin. If it is not linearly separable, a kernel trick is use (radial basis function kernel or polynomial kernels). If we want to interpret individual model predictions in a comprehensible and trustworthy way, we need robust explainability methods. In our work, we considered two commonly used approaches: LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive ExPlanations). LIME is a method used to explain the prediction of a specific data instance by building a simple linear model that locally approximates the behavior of a complex (black box) model [3]. It generates new samples around the selected instance, obtains their predictions using the original model, and assigns weights to these samples based on their similarity to the original instance. The most influential features are then selected, and a weighted linear model is trained to approximate the complex model's decision in that local region. The result is a local explanation that highlights which attributes most influenced the model's decision for the selected case, providing a clear and human-understandable interpretation. SHAP is a method based on Shapley's values from game theory [41]. [PERSON] [10] describes that SHAP provides both global and local explanations of ML models. The SHAP method comes with both local and global visualizations based on the aggregation of Shapley values. The Shapley value is a concept derived from game theory that describeshow a \"payout\" can be fairly distributed among individual \"players.\" Thus, if we transform it into ML, it is a question of how the individual attributes contribute to the prediction of the ML model. The attribute's contribution to the model's predictions is obtained as a weighted average marginal contribution of the attribute calculated based on all possible combinations of attributes. As a result, as the number of attributes increases, the time required to calculate Shapley values increases. The SHAP method provides us with many different visualizations, whether used for global or local interpretation of ML models [10]. Currently, several authors have focused on more understandable models and the evaluation of their explanations. We found that several metrics are important in the evaluation of the quality of explanations and the comprehensibility of explainable models, but for this work we decided to apply five existing metrics and proposed two new metrics that could contribute to achieving a better and faster evaluation of the resulting models to obtain the most understandable and high-quality explainable models: accuracy, identity, separability, stability, understandability, user experience, response time. The existing metrics:  
* Accuracy--using this metric, we will evaluate whether individual models are suitable, functional, and achieve high-quality accuracy [42]. * Identity--this metric can help us to make sure that there are two identical cases that exist in the data, and if so, they must have identical interpretations [43]. This means that for every two cases in the test dataset, if the distance between them is equal to zero, that is, identical, then the distance between their interpretations should also be equal to zero.