Articles | Volume 14, issue 3
Model evaluation paper
16 Mar 2021
Model evaluation paper |  | 16 Mar 2021

Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China

Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel

Data sets

XGBoost-SHAP values, prediction of grassland degradation Batunacun and R. Wieland

Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.