… Moreover, we also employ the newly developed Shapley additive explanation (SHAP)
approach to tackle the non-interpretability issues of the ML algorithm by interpreting the XGBoost …

… of Shapley Interaction Values towards n-Shapley … (Shapley, 1953), and our work builds on
a particular paper from this literature, namely the seminal work by Grabisch (1997) on additive …

… However, we show how Shapley additive explanations can be used to interpret the outputs
of a deep neural network designed to predict Nitrogen dioxide concentrations in Madrid. This …

… the effects of time-related variables, including fluctuations in climate, alterations in road
infrastructure, and advancements in vehicle safety technology throughout the research duration. …

… information, such as cardiopulmonary complications related to variables including smoking
index and smoking cessation duration [35], which may help improve prediction efficiency.…

… using a linear explanatory model as well as the Shapley values, as shown in Eq. (8)(8) γ z
… A convolutional neural network approach with Shapley additive explanations interpretability …

… We designed a risk prediction model based on random forest algorithm with SHapley
Additive exPlanations strategy and developed a stadium fire risk assessment model. It can …

… This study aims to establish and validate PVD risk prediction models and perform risk
factor analysis for PVD in patients with T2DM using machine learning and Shapley Additive …

… Moreover, the SHapley Additive exPlanation method results indicate that the important
predictors are mainly from the Southern Hemisphere, Eurasia, and western Pacific, and most …

… with NaOH concentration and pretreatment duration has been meticulously compiled for …
, RF model-based feature importance, and Shapley additive explanation from global or local …