## 3 Results and Discussion
Similarly, out of a total of 48 AC roofs labeled with HIP, 44 were correctly classified, and 4 were incorrectly classified. These results indicate, in general terms, that if the model receives information corresponding to a roof with HIP as input, it has a high tendency to recognize it as a roof with HIP (close to 9 out of 10 cases). On the other hand, when the model receives input data for a roof with LIP, it can recognize it in 6 out of 10 cases. To refine the analysis, precision, recall, and F1-score, metrics were derived from the confusion matrix for labels 1 (roofs with LIP) and 0 (roofs with HIP), as shown in Table 4.  
The analysis of evaluation metrics for the prediction model of asbestos-cement (AC) roofs with LIP and HIP reveals significant aspects of its performance. Firstly, for the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Precision** & **Recall** & **F1-Score** \\ \hline LIP & 0.604 & 0.879 & 0.716 \\ HIP & 0.917 & 0.698 & 0.793 \\ Average & 0.760 & 0.789 & 0.754 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of metrics derived from the confusion matrix. Figure 8: Scheme of the decision tree obtained. classification of roofs with LIP (1), the model exhibits a precision of 60.4%, indicating that around 60.4% of positive predictions are correct. This implies an acceptable performance in identifying roofs with LIP out of the total roofs with LIP. This result is complemented by a high recall of 87.9%, showing that the model is highly effective in not confusing roofs with HIP with roofs with LIP. In the same vein, the harmonic mean of precision and recall, expressed through the F1-score, is 0.716, indicating a balance between these two crucial metrics for the evaluation of classification models. On the other hand, in the classification of roofs with HIP (0), the model exhibits a notable precision of 91.7%, indicating that a high percentage of negative predictions are accurate. This means that the model is effective in distinguishing roofs with HIP from the total roofs with HIP. However, the corresponding recall is 69.8%, suggesting that the model tends to confuse 30% of roofs with LIP as roofs with HIP. The weighted average of precision, recall, and F1-score, obtained through averaging, is 0.760, 0.789, and 0.754, respectively, providing a comprehensive assessment of the model's performance in both classes. This implies that, overall, the classification ability of the model regarding the condition of asbestos-cement roofs is good. The observed discrepancy may be attributed to the greater variability in the spectral signatures of LIP roofs, as reported in a previous study [35]. In contrast, the spectral signatures of HIP roofs exhibit less variability, leading to more homogeneous profiles. This increased variability in LIP signatures results in overlapping zones that complicate the differentiation between HIP and LIP using the current methodology. It is recommended that future studies develop approaches to isolate these classification errors and verify the actual condition of the roofs through field assessments. The results obtained in this research allowed determining, through the InfoGainAttributeEval and Ranker methods, the reflectance bands that are most relevant within the dataset formed for the identification of the condition of asbestos-cement (AC) roofs. Additionally, the best models were identified in different groups of relevant bands, revealing that the decision tree model exhibits the best performance for the group of 10 relevant bands, with cross-validated accuracies exceeding 76%. Furthermore, the performance of the random forest model is noteworthy, showing cross-validated performances exceeding 71% for the group of 30 relevant bands. In the same vein, the inference rules derived from the best decision tree model were identified, allowing the identification of the ranges of normalized values that a select set of bands must have to determine the condition of an asbestos-cement roof.