## 4 Results
### Models and Statistical Validation  
The machine learning models were trained independently using each of the six sets of predictors individually. Figure 4 shows a comparison of the different LG models with the training data. To establish the best model, both the error and AUC of each model were needed for analysis. This information is also provided in Table 4, along with the combined performance. Both criteria are consistent since combined performance is a combination of model accuracy and discrimination ability (represented by AUC), whereas error and AUC are independent measures. A model can have a high AUC and a high error rate, which means it has good discrimination ability but may misclassify some instances. Similarly, a model can have a low AUC and a low error rate, which means it has high accuracy but low discrimination ability. Therefore, a model with a low error and high AUC is considered better than the others. According to these criteria, all SETs are good in terms of AUC and error rate (above 94% and below 14%, respectively), apart from SET-1 and SET-4, which experience a decrease in such scores. The model using the predictor SET-2 shows the lowest average error (12.74%) and the highest average AUC (94.19%), being the best model. It is important to note that, although the model using the variables selected via the genetic algorithm (SET-5) has the second-best performance in terms of AUC (94.16%), its performance in terms of error is considerably inferior to that of the selected model (12.91%). Combined performance results also show the same trend, with SET-2 as the model that has the highest performance rate value (82.19%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VARIABLE** & **SET-1** & **SET-2** & **SET-3** & **SET-4** & **SET-5** & **SET-6** \\ \hline Dist\_falla & X & X & X & X & X & X \\ Dist\_vial & & & & & X \\ Dist\_cuce & X & & X & & X & X \\ Dist\_epi & & & & & & X \\ Pend & X & X & X & X & X & X \\ Orient & & X & X & X & X & X \\ Curvar & & & & & & X \\ Pla & & & & & X & \\ Perfil & X & X & X & X & X & \\ Rugos & X & X & X & & X & X \\ Ac & & & & & & X \\ Fs & & & & & & X \\ Ia & X & X & X & X & X & X \\ Pga & X & X & X & X & X & X \\ Z & & & & & & X \\ Geology\_Carbonates\_rocks & & & X & & & X \\ Geology\_Quartzites\_and\_schists & & & X & & & X \\ Geology\_Conglometers, sandstones\_and\_clays & & X & X & X & X & X \\ Geology\_Marts & & X & X & X & X & \\ Geology\_Alluvial & & X & X & X & X & X \\ Landform\_Very\_deep\_canyons\_and\_ravines & & X & & X & X & X \\ Landform\_Mountain\_tops\_and\_high\_ridges & & & X & & X & X \\ Landform\_U-shaped\_valleys & & & & X & X & X \\ Landform\_Flats & & X & & X & X & X \\ Landform\_Open\_slopes & & X & & X & X & X \\ Landform\_High\_slopes\_and\_plateaus & & X & & X & X & X \\ Landform\_grouped & & & & & & X \\ Sr08\_1. less\_than\_1 & & X & X & X & X & X \\ Sr08\_1. from\_1\_to\_15 & & X & & X & X & X \\ Sr08\_1 greater\_than\_or\_equal\_to\_15 & & & X & & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictor distribution for each of the six sets established in the study. Figure 5 compares the best RF model already adjusted based on the'mtry' parameter to the training data. In general, all the models achieve a good score in terms of combined performance (above 84%), except for SET-4 (Table 5). This behavior may reflect that the parameter combination adopted by the corresponding predictors does not work as well as the other combinations analyzed with different predictor sets. Under the criteria of high AUC and low error, the best model corresponds to SET-3, with'mtry' value of 4. This model achieved 95.31% AUC and a 10.89% error rate (Table 5). Under the criteria of combined performance, the best model is also SET-3, as it has the highest value (84.94%), followed by SET-5 and SET-2. This indicates that SET-3 has the most balanced combination of accuracy and discrimination ability, whereas SET-5 and SET-2 have slightly better accuracy than SET-3, but with slightly worse discrimination ability.