## 4 Discussion  
### Results Interpretation  
The results obtained corroborate the efficiency and good performance of XGBoost; however, with the sets of factors proposed in this study, the performance obtained was lower than that obtained considering the 15 factors for which information is available [7], whose AUC = 0.87. When considering only the six best factors previously determined [7] and with the hyperparameter test implemented to optimize performance, an AUC of 0.83 was obtained with the first sampling, this being the best performance obtained with all the data sets applied. This verifies the veracity of the results obtained with the feature selection algorithms applied in the previous stage of this research; however, it is observed that the overall performance of the algorithm (based on its AUC) has not improved (even it has decreased slightly) when implementing the hyperparameter tuning, which makes it necessary to go deeper into these tunings to understand the causes of this behavior. With the same set of factors, but with the second sampling, the performance shows some decrease (AUC = 0.81), however, it is still a good predictive model according to the values of the implemented metrics. Additionally, with these two data sets, the F-Score value is the highest (0.73 and 0.72, respectively), which supports the good predictive capacity of the model with these data sets, although it is also lower than that obtained with the 15 factors (0.78). On the other hand, the coincidence of landslides in the high and very high susceptibility levels exceeds 90% when considering the first sampling, which is an additional indication of the predictive efficiency of the model. In this case, this percentage is higher than that obtained with the 15 factors, which was approximately 78%. With the second sampling, the coincidence in the aforementioned susceptibility levels decreases, although it is still acceptable since it presents a value of approximately 73%. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **6 Factors (S1)** & **6 Factors (S2)** & **DEM (S1)** & **DEM (S2)** & **NO DEM (S1)** & **NO DEM (S2)** \\ \hline
6 factors (S1) & 0 & 0.0156 & 0.0122 & 0.0083 & 0.0275 & 0.0081 \\
6 factors (S2) & 0 & 0.9618 & 0.4076 & 0.3633 & 0.0386 \\ DEM (S1) & & & 0 & 0.8338 & 0.7432 & 0.7544 \\ DEM (S2) & & & 0 & 0.3870 & 0.8453 \\ NO DEM (S1) & & & & 0 & 0.1212 \\ NO DEM (S2) & & & & 0 \\ \hline \hline \end{tabular}  
* S1: Sampling 1. S2: Sampling 2. DEM: DEM-dependent conditioning factors. NO DEM: No DEM-dependent conditioning factors.  
\end{table} 
Table 9: Wilcoxon test results for susceptibility models. The results obtained with DEM-dependent factors set show a much lower performance, which allows inferring that these factors, although important, are not the only ones that should be considered to implement LSP in the study area. When applying this set of factors, the first sampling yielded an AUC of 0.68, a value that could not be considered acceptable; however, when considering the second sampling, the performance shows a certain improvement (AUC = 0.73) that would be considered acceptable. In the case of this data set, F-Score presents the lowest values of all the data sets applied. In this sense, the F-Score value with the first sample is 0.61, and with the second sample, it is 0.63. Although the metrics present low values, it is striking that the coincidence of landslides in the susceptibility levels is not low, since in the first case more than 87% of landslides fall in high and very high susceptibility zones; whereas, in the second case, this coincidence decreases, since approximately 60% of landslides coincide with such levels, being the lowest value of all the analyzed data sets.