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<strong class="journal-contentHeaderColor">Abstract.</strong> The exploration of shallow landslide susceptibility is often impaired by incomplete and imperfect landslide inventories, and by over-optimistic performance metrics linked to inadequate models. In this article, we make use of a recently published, systematically mapped event inventory containing 571 shallow landslides triggered in southern Norway and apply a total of 32 gradient boosted decision tree models to rigorously test the effects of (1) a nested vs. simple cross-validation strategy, (2) spatial vs. non-spatial models, (3) four different cross-validation sampling strategies which were applied on (4) full vs. forest-only datasets. Model evaluation shows that the spatial model with small block nested cross-validation is a suitable compromise between modelling the spatial structure of the landslide data adequately while retaining realistic predictive power. The compromise models suggest that soil thickness explains landslide probability partly, while other important explanatory factors like elevation, aspect and bedrock weatherability serve as soil indicators, illustrating a need for improved datasets for soil thickness and heterogeneity. In the forest-only models, tree type explains landslide probability best, driven by greater landslide susceptibility in deciduous forest. Presented results suggest that susceptibility mapping may be improved significantly by considering forest variables and forest-specific threshold values.