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When a CBR in Hand is Better than Twins in the Bush

Publication Type:

Conference/Workshop Paper


Fourth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems


AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off has been shown not to exist. This paper discusses a regression problem context where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the global importance of features from XGBoost into global weights of the CBR model, we found that the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model can be seen as a benchmark for this problem context, we compare the two additive feature attribute explanation models SHAP and LIME as explanation models for the XGBoost model. We examine the results and identify potentially valuable future work.


author = {Mobyen Uddin Ahmed and Shaibal Barua and Shahina Begum and Mir Riyanul Islam and Rosina O Weber},
title = {When a CBR in Hand is Better than Twins in the Bush},
month = {September},
year = {2022},
booktitle = {Fourth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems},
url = {}