Fig. 3

Interpretability of predictors in the XGBoost predictive model during 5-fold cross-validation. The vertical axis lists a series of features arranged by the average magnitude of their SHAP values. The horizontal axis represents the SHAP value for each feature, indicating the impact of each feature on the model’s prediction. Larger absolute SHAP values indicate a more significant effect. Points represent the SHAP values for individual samples across features. Point position corresponds to the SHAP value on the horizontal axis and the feature on the vertical axis. The colour of the points indicates the impact level: red signifies a high impact (High), meaning that the feature significantly increases the model’s output or response rate; blue represents a low impact (Low), meaning that the feature significantly decreases the model’s output or response rate. This visualisation helps identify which features most influence the model’s predictions and how different feature values (high or low) affect individual predictions. It provides an intuitive understanding of the model’s behaviour, facilitating stakeholder communication