## 5 Materials and Methods  
First, data preparation was done, thus creating the files to be exploited by WEKA and ELVIRA [39, 40] tools. WEKA is a collection of automated learning and data preprocessing algorithms ([PERSON], [PERSON] et al. [41]) developed by the University of Waikato in New Zealand and has tools for data reprocessing, classification, regression, clustering, association rules, and visualization. It is an open-source software under the general public license of GNU (GPL). ELVIRA is the result of a project funded by the Interministry Commission of Science and Technology (CICYT) and the Ministry of Science and Technology of Spain, of which researchers from different Spanish universities--the Universidad de Educacion a Distancia (UNED) among others--were part. The tool \"is used for editing and evaluating probabilistic chart models, specifically Bayesian networks and influence diagrams\" ([PERSON], [PERSON] [42]). Second, the data analysis was performed using the WEKA tool in Explorer mode, which allows data preprocessing, filter application, classification, clustering, association rule, attribute selection, and data visualization tasks ([PERSON], [PERSON] [43]). As an example, Figure 1 shows the main window with a data file loaded with summer data. It is later replicated with winter data [39, 40]. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Thermal Comfort Form—Summer/Winter** & & & \\ \hline Region: & & & \\ \hline Company/Institution & & & \\ \hline Address: & & & \\ \hline Email Address: & & & \\ \hline Area: & & & \\ \hline Activity: & & & \\ \hline Day and Time: & & & \\ \hline Personnel & Temperature & Relative Humidity & Status \\  & (\({}^{\circ}\)C) & (\%) & (1 to 10) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermal Comfort Interview. Third, a classification algorithm, J48 [43], was used to make a decision tree. In this sense, in an ideal situation with an infinite number of cases, predictions on quality measurements of model behavior would be perfect. In all practicality, the data set must be divided. Thus, stratified cross-validation was used. It is used when data are divided in an \"n\" number of parts and, for each division, the classifier is built with the remaining n\(-\)1 parts, and it is tested. The procedure is repeated for each of the parts. A cross-validation is said to be stratified when each of the parts has characteristics of the original sample. The sample attribute is then selected--generally the last one on the list, which is the variable to be determined in the classification. Cross-validation results for the J48 Classifier are available on ResearchGate [39,40]. When finished, a decision tree is shown in the \"Visualize tree\" option if it was created from the classifier. Figure 2a,b shows the decision trees for summer and winter, respectively. Fourth, to execute ELVIRA, the data files were built from the WEKA decision trees for the northern area in summer and winter, as mentioned in \"Evaluation, using Algorithms, of Thermal Comfort Levels in the Industrial Area of the Region of Buenos Aires, Argentina\" [28]. As mentioned, ELVIRA allows editing and assessing probabilistic graphic models--specifically, Bayesian networks [42]. The Bayesian inference is a probabilistic reasoning that spreads the evidence effects through the network to know the \"a posteriori\" variable probability [17]. Bayesian classifiers can be considered a special case of a Bayesian network, in which there is a specific variable (class) and the rest of the variables are attributes. Bayesian classifiers are widely used due to the advantages they offer, such as, their simplicity for building and understanding, their reliability when assessing irrelevant attributes, and their pondering of several properties to provide a final prediction. In this study, data preprocessing was performed, followed by machine learning operations, in which the Naive-Bayes classifier (NBC) was chosen.