Landslides have caused huge economic and human losses in China. Mapping of landslide susceptibility is an important tool to prevent and control landslide disasters. The purpose of this study is to make use of a hybrid machine learning approach by combining the reduced-error pruning trees (Rept) with a series of ensemble techniques (Bagging, Dagging, and Real Adaboost) and compare the performance of each combination for landslide susceptibility modeling. The combination of Rept model and Real Adaboost (RRept)is a novel application in the field of landslide susceptibility. Firstly, a landslide inventory map was prepared with 298 determined historical landslides events in the study area, 209 landslides (70%) were randomly selected for the training dataset and the remaining 89 landslides (30%) were used for validation dataset. On this basis, 16 landslide influencing factors were included in the landslide susceptibility evaluation (slope angle, elevation, slope aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to roads, distance to faults, soil, normalized difference vegetation index (NDVI), landuse, lithology and rainfall). Secondly, the correlation attribute evaluation (CAE) method was used to select the most important factors for the proposed landslide susceptibility model. The results show that all the factors contribute to the occurrence of landslide. Slope angle, Landuse, Elevation, Distance to roads, Soil and Lithology have the greatest influence on the occurrence of landslide. The receiver operating characteristics (ROC), standard error (SE), 95% confidence interval and mean absolute error (MAE) were then used to validate and compare the performance of the model. The best model should have the largest AUC value, the smallest SE, the narrowest 95% CI and the smallest MAE. The results show that the three hybrid models perform better than the Rept model alone. For the training data set, the RRept model has highest AUC value (0.927), the smallest SE (0.121), the narrowest 95% confidence interval (0.898–0.95) and the lowest MAE (0.20). For the validation data set, the RRept model has the highest AUC value (0.745), the narrowest 95% confidence interval (0.674–0.807) and the lowest MAE (0.33). The RRept has the highest predictive power for landslide susceptibility evaluation. The results show that a hybrid method improves the prediction ability of the base Rept model. In addition, the RRept model is a promising comprehensive model that can be applied to landslide susceptibility mapping.