Evaluation of the Shapley Additive Explanation Technique for Ensemble Learning Methods

Evaluation of the Shapley Additive Explanation Technique for Ensemble Learning Methods

This study aims to explore the effectiveness of the Shapley additive explanation (SHAP) technique in developing a transparent, interpretable, and explainable ensemble method for heart disease diagnosis using random forest algorithms. Firstly, the features with high impact on the heart disease predi...

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Bibliographic Details
Main Author: Tsehay Admassu Assegie
Format: Article
Language:English
Published: Taiwan Association of Engineering and Technology Innovation 2022-04-01
Series:Proceedings of Engineering and Technology Innovation
Subjects:
model explanation
random forest
ensemble method
interpretability
Online Access:https://ojs.imeti.org/index.php/PETI/article/view/9025

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