## 1 Introduction
[PERSON] et al. [5] generated LSM using genetic algorithms in Macka (Turkey). Their 16 factors were classified according to the dependence on a Digital Elevation Model (DEM) or those obtained from satellite images. [PERSON] et al. [20] implemented LSP combining 14 conditioning factors (morphometric and non-morphometric) in Sajadrood (Iran) applying ML. In the same area, [PERSON] et al. [22] compared three sets of conditioning factors for LSP. [PERSON] et al. [23] identified essential conditioning factors for LSM in Wushan and Wuxi (China), grouping 28 factors into geological, environmental, meteorological and human activities. [PERSON] et al. [15] studied 15 conditioning factors for LSP generation in Chongyi (China), classifying them into topographical, geological, hydrological, anthropogenic and land occupation. Of all these studies, none applied XGBoost to obtain LSP. Previously [7], XGBoost was determined to be the best performing ML algorithm in the study area. Therefore, it was further analyzed in this research. Although XGBoost performs optimally for different types of problems [24], it has recently been implemented in some LSM studies [25; 26; 27; 28; 29; 30; 31]. In all these cases, XGBoost has presented good performance, having as an advantage its ability to avoid overfitting with proper configuration of its regularization hyperparameters [32], although despite its versatility and good performance [33], it can be computationally demanding [24]. There are several approaches to optimize hyperparameters, with Bayesian Optimization (BO) being widely used [29]. BO has been implemented in studies applying XGBoost for LSM [29; 34]. It is highlighted that XGBoost significantly reduces overfitting when the configuration of its hyperparameters is appropriate [35], therefore, it is necessary to implement models that consider this test to obtain computationally affordable performance with acceptable prediction results. This research aligns with previously published studies [7; 12] on LSM with ML. For further research, XGBoost was chosen for LSM since this method is not too widely employed and generates good results. First, an optimization of this algorithm was applied by testing its main hyperparameters with a trial-and-error method to verify its computational performance and predictive capability. Second, this study analyzed combinations of conditioning factors to obtain LSM with XGBoost. Out of 15 conditioning factors, the combinations were based on the dependent (ten factors) and non-dependent (five factors) factors of the DEM in a sector of Cuenca, Ecuador, which has no previous studies. Two different random sampling methods were also evaluated and applied to each combination of conditioning factors to verify variations in LSP. Finally, a statistical analysis was performed using the Wilcoxon test to verify the significance of the susceptibility levels determined by each model. The main contribution, besides testing the efficiency of XGBoost optimized for LSM, is to determine whether combinations of conditioning factors based on one criterion (dependence and not on a DEM) and with different types of sampling, improve the performance of LSP in the study area, compared to the results achieved when considering the six most relevant conditioning factors, obtained with the implementation of feature selection algorithms.