Different sampling strategies produce varying sample data, serve as the primary input data and directly affect the accuracy of predictions in data-driven grid-based susceptibility models. This study analyzes the accuracy and variation of debris flow susceptibility maps (DFSMs) generated by various sampling strategies. The study area is the Yingxiu region in China, where six sampling strategies were applied, including three sampling locations (deposition area, runout area, and source area) and two sampling types (centroid and polygon) for the debris flow inventory. The effectiveness of 10 conditioning factors used to build the model was assessed by using Pearson correlation coefficient, variance inflation factor, and information gain ratio (IGR) techniques. We then used Weight of Evidence (WofE), Logistic Regression (LR), and Deep Neural Network (DNN) models to produce DFSMs and quantify their performance using the receiver operating characteristic curve (ROC), Accuracy (ACC), Precision, F1 score, and Recall. The results show that the WofE (AUC: 0.754-0.960), LR (AUC: 0.761-0.965), and DNN (AUC: 0.786-0.976) models all perform well, but the DFSMs and dominant factors depend strongly on sampling strategies, especially on sampling location. If the sample areas are excessively large and span across different factor class labels, or if there is a concentration of either large or small sample areas within a specific region, the results of centroid and polygon sampling strategies may differ or even be contradictory.We recommend: (1) determining sampling locations based on the research objectives to provide more accurate evaluation results; (2) selecting the sampling type by first considering the sample size. If the aforementioned conditions are not present, the quicker and more convenient centroid sampling strategy can be chosen; and (3) determining an appropriate sampling strategy and ensuring the accuracy of initial samples are paramount before producing DFSMs.