# Intelligent System Using Data to Support Decision-Making
## 2 Materials and Methods
These values are organized in a decision matrix \(Y=y_{ij}\), where \(y_{ij}\) represents the value of the \(i\)-th alternative with respect to the \(j\)-th criterion. The method is based on selecting the alternative that is closest to the ideal alternative, represented by the vector \((H_{1},\ H_{2},\ H_{3}\,\ \ldots\ldots\ H_{k})\) and farthest from the worst alternative, represented by the vector \((D_{1},\ D_{2},\ D_{3}\,\ \ldots\ldots\ D_{k})\). At the end of the count we arrange the alternatives according to decreasing values of the relative distance indicator \(c_{i}\), thus obtaining a complete arrangement of all alternatives. The Borda count is a ranking method developed by [PERSON] in the 18 th century [31]. It is used in decision-making when multiple alternatives need to be evaluated based on voters' preferences. Each voter ranks the alternatives from most to least preferred. Points are then assigned based on position:  
* if there are \(n\) alternatives, the top-ranked receives \(n-1\) point,
* the second \(n-2\), etc.,
* with the last receiving 0 points. After summing the points from all voters, the alternative with the highest total is ranked first. The Borda method is particularly useful when aggregating preferences from multiple decision-makers and when the overall ranking of options matters. After thoroughly analyzing existing solutions, understanding several metrics to evaluate the quality and understandability of individual models, we decided to apply the following machine learning algorithms: Random Forest, Decision Tree, Logistics regression, k-NN, SVC. Decision Trees (DT), according to prof. [PERSON] [32], and also [PERSON] and [PERSON] [33], represent one of the most popular approaches for solving classification tasks, as the extracted knowledge is simple, intuitive, and easy to interpret. They provide end users with a clear graphical representation of the results and usually allow for quick and straightforward evaluation of the acquired knowledge. Random Forest (RF) is a method for building predictive models by combining multiple simple Decision Trees [34]. It is a bagging-based technique that constructs a large collection of de-correlated trees (i.e., independent from each other) and averages their outputs. The classifier consists of many unpruned trees. A higher number of trees results in more predictions and thus improved accuracy through majority voting, compared to individual trees. Since DT, whether for regression or classification, tends to be noisy, Random Forests significantly improve performance by averaging, which reduces variance. Logistic Regression (LR) gained broader application in statistics and machine learning during the 20 th century, particularly after the work of [PERSON] in 1944, who formalized its use for classification tasks in medicine and industry [35]. According to [PERSON] et al. [36], LR is a statistical model used to classify and predict the probabilities of binary or multinomial outcomes. Despite its name, Logistic Regression is primarily used for classification, not for predicting continuous values as in linear regression. The model employs a logistic (sigmoid) function to transform input values into the interval (0,1), which is then interpreted as the probability of a given class. K-Nearest Neighbors (k-NN) is a simple and efficient nonlinear classification and regression algorithm that belongs to the lazy learning group [37]. It works on the principle of finding the k-nearest neighbors of a new sample based on the distance in the feature space and assigning a class or predicting values based on their majority voting (in classification) or averaging (in regression). The k-NN algorithm was first introduced in 1951 in a research report by [PERSON] and [PERSON] [38]. It was subsequently expanded and popularized in a 1967 paper by [PERSON] and [PERSON] [37], who formally proved its optimality properties. Support Vector Machine (SVM) [39] is an algorithm used for classification and regression.