###### Abstract
These advantages make them an ideal choice for the rapid analysis of landslide susceptibility on a large scale [9]. However, the performance and applicability of different machine learning models vary significantly, making it crucial to distinguish between them to optimize prediction accuracy. Previous studies have employed various machine learning techniques for landslide assessment. Support Vector Machine (SVM) [10; 11], Random Forest (RF) [12], XGBoost [13], artificial neural network (ANN) [14; 15], and others are among the commonly utilized machine learning models. These models have shown different performances and advantages in various studies, demonstrating their broad application potential in landslide susceptibility evaluation. Additionally, numerous scholars have developed novel risk assessment methods based on machine learning [16; 17]. For example, [PERSON] et al. [18] utilized innovative hyperparameter optimization methods (such as halving search, grid search, random search, fine-tuning method, and k-fold cross-validation) in Python to develop a risk assessment support tool for deriving seismic fragility curves. Compared to traditional seismic fragility assessment methods, these optimized machine learning methods significantly reduce computational efforts and exhibit superior performance. They achieve satisfactory accuracy and closely match the actual curves. The establishment of these risk assessment methods provides a scientific foundation for disaster prevention and mitigation. Meanwhile, the application of machine learning models has also enhanced the accuracy and efficiency of the assessments. Constructing a machine learning sample set is a significant process in using machine learning models. Currently, the landslide sample sets used in research are typically manually interpreted and compiled based on historical data. In the study of specific landslides, determining their boundaries is not a difficult task. However, in regional landslide susceptibility studies, the lack of historical data and the complexity of the natural environment can lead to errors in determining landslide boundaries. Furthermore, the process of remote sensing interpretation for numerous landslides consumes a considerable amount of time and resources. Therefore, in practical research, points are often used to replace irregular landslide boundaries [19]. The high-precision prediction of machine learning models relies on effective training and testing of the model. To support this, it is necessary to select a non-landslide sample set that matches the landslide sample dataset. However, the sampling methods for negative samples are currently mostly based on subjective speculation or random selection. This can result in negative samples being biased towards potential landslide points and locations, introducing uncertainty into the model and causing differences in landslide risk prediction results. The core focus of this study is to investigate the impact of negative sample selection on model predictions and strive to minimize biases introduced by sample selection on model outcomes. The study proposes a modeling process for landslide susceptibility based on machine learning algorithms, integrating the information value method and frequency ratio method to optimize sample selection and model input variables. Using trained models, the study predicts landslide susceptibility indices and generates maps, followed by a comparative analysis of evaluation results.