Interpretability study on prediction models for alloy pitting based on ensemble learning

Abstract

Ensemble learning (EL) models were developed to predict the pitting potential (Ep) of alloys. By carefully selecting features and optimizing parameters, the predictive accuracy of the models was improved and experimentally validated. Interpretability methods, SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Accumulated Local Effect (ALE), explained the model's prediction logic effectively and portrayed the effects of alloying elements, thermodynamic parameters, and environmental factors on Ep in detail. Furthermore, performance differences between interpretability methods, errors in model prediction, and failure cases of interpretability methods were discussed, which provided insights for model optimization and appropriate Interpretability methods selection.