## 3 Materials and Research Methods
### Creating a Model for \(QoE_{i}\) Prediction and Classification  
Models for \(QoE_{i}\) Prediction  
As part of the research, predictive \(QoE_{i}\) models based on multiple linear regression, decision trees and machine learning models were created in the IBM SPSS Modeler software environment using an automatic modeling method [40]. The data obtained by filling out the questionnaire along with the estimated \(QoE_{i}\) values were structured in an Excel file into input/output vectors and divided into two parts. Ninety percent of the vectors were used to train the model and the remaining ten percent were used to test the prediction accuracy performance. As input to all created models, 10 independent variables (X\({}_{1}\)... X\({}_{10}\)) were used. All models were created according to the supervised learning paradigm [39].  
A multiple linear regression model was created in Minitab statistical software. The coefficients of the model were determined via the method of least squares.  
A machine learning model based on decision trees (_boosted decision tree_) was created in the MATLAB software package.  
Predictive models created by using an automatic modeling method: In the IBM SPSS Modeler operating environment, structured data for training and testing were brought to the input of the Auto Numeric node in order to automatically create different predictive models simultaneously. In this way, in just one pass through the modeling process, the Auto Numeric node examines different machine learning techniques with default option settings, and based on that, offers the most accurate potential solutions and ranks them according to correlation and relative prediction error [24]. Supported techniques include neural networks, classification and regression trees (C&R tree), CHAID (Chi-square automatic interaction detection), linear regression, generalized linear regression and SVM. The relative error (\(RE\)) of the prediction was used as a criterion for quality assessment and selection of the most accurate model [24]. \(RE\) represents the ratio of the sum of squared errors ofthe dependent variable \(QoE_{i}\) and the sum of squared errors of the null model, or, given mathematically:  
\[RE=\frac{\sum\limits_{i=1}^{n}{(QoE_{i}-QoE_{ip})^{2}}}{\sum\limits_{i=1}^{n}{(QoE _{i}-QoE_{im})^{2}}} \tag{4}\]  
where \(QoE_{i}\) is the estimated value of user experience of the \(i\)th user, \(QoE_{ip}\) is the prediction based on the actual value of \(QoE_{i}\), and \(QoE_{im}\) is the arithmetic mean of the variable \(QoE_{i}\).  
In order to increase the accuracy of the model prediction, the DA method was used to artificially expand the available dataset [41; 42]. Synthetic data were generated using the Autoencoder artificial neural network implemented in the MATLAB programming environment, which was trained according to the reinforcement learning paradigm.  
Additional improvement of the accuracy of predictive models in the IBM SPSS Modeler software was carried out using the boosting method. This method is based on the creation of several models (components) in sequence, i.e., creating an ensemble of models that, as a result, provide a joint prediction of the dependent variable.  
\(QoE_{i}\) classification models created by using an automatic modeling method.  
\(QoE_{i}\) classification models, as well as predictive models, were created in the IBM SPSS Modeler software environment using the _Auto Classifier_ option. Support techniques included neural networks, C&R trees; quick, unbiased, efficient statistical trees (QUESTs); CHAID; C5.0; logistic regression; decision lists; Bayesian networks; discriminants; nearest neighbors and SVM. The DA method was used to improve classification performance by artificially expanding the training and testing datasets. A set of 10 independent variables was used as an input to the classification models, and the dataset was divided in the ratio 90%:10%.