## 3 Results and Discussion
Figure 4: Spectral signatures of asbestos–cement (AC) roofs. (**a**) with LIP and (**b**) with HIP. Building upon the merit metric obtained via the InfoGainAttributeEval evaluator, along with the attribute ranking generated with the Rank search method, the evaluation of five supervised learning models (decision trees, KNN, logistic regression, support vector machine, and random forest) was carried out using a five-fold cross-validation method. This aimed to assess the impact of varying different band groups on the model's performance. Thus, starting from the top 10 attributes, the evaluation extended to encompass all attributes or bands in the dataset, increasing by 20 bands at a time. For each band group, the maximum accuracy value achieved with any of the five evaluated supervised learning models was determined. In Figure 7, a graph is presented illustrating, for each attribute group obtained from the ranking of the bands (10, 20, 30, 50, 70, 90, 110, 130, 150, 170, 190, 210, 230, 250, 270, 290, 310, 330, 350, 370, and 379), the best performance as a percentage (%) determined in cross-validation from the five considered models. From Figure 7, it is evident that the highest accuracies in cross-validation are obtained for the top 10, 20, and 30 attributes or bands determined via the information gain-based evaluation method. For the group of 10 bands or attributes, the highest accuracy is achieved with a precision value exceeding 76%. Figure 5: Application of the InfoGainAttributeEval selection method on the asbestos–cement (AC) roofs dataset. What is presented in Figure 7 can be more clearly observed in Table 1, where the results of each of the five considered supervised learning models are independently displayed for each attribute group obtained via the information gain-based evaluation method. It is  
Figure 6: Merit obtained for different frequency bands. Figure 7: Accuracies obtained in cross-validation for different groups of bands. It can be observed that within the top 10 bands, 7 fall within the range between 104 and 110, while 3 are situated between 220 and 222. Additionally, it is noteworthy that these bands have a merit metric value close to 0.39. Starting from the top 10 bands presented in Table 3, a subset of the dataset was obtained, consisting of 11 columns (the top 10 bands and the roof type column) and 96 rows. From this subset, the decision tree model presented in Figure 8 was adjusted and obtained. This model enables the classification of AC roofs with low and high intervention priority with an accuracy exceeding 76%. The tree in Figure 8 indicates that bands 110 and 105 are crucial in identifying asbestos-cement (AC) roofs with LIP or HIP. Accordingly, an AC roof can be considered with LIP if the normalized value of band 110 is less than or equal to 0.415409, and the normalized value of band 105 is greater than or equal to 0.3174. Similarly, an AC roof can be considered with HIP if the normalized value of band 110 is greater than 0.4154, or if band 110 is less than or equal to 0.4159 and band 105 is less than or equal to 0.3174. Finally, in Table 3, the confusion matrix obtained for the decision tree model with the top 10 bands determined via the estimator is presented. The confusion matrix was utilized to evaluate the best model obtained in cross-validation, considering that it provides a clear insight into the accuracy and classification ability based on the breakdown of true positives, true negatives, false positives, and false negatives. The significance of the confusion matrix lies in its ability to reveal both the strengths and potential weaknesses of the models, enabling an informed decision-making process in the continuous improvement of system performance. The confusion matrix obtained for the decision tree model reveals, in terms of evaluation, that out of a total of 48 asbestos-cement (AC) roofs labeled as LIP, 29 were correctly classified, and 19 were misclassified as HIP.