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AI methods referred to as interpretable are often discredited as inaccurateby supporters of the existence of a trade-off between interpretability andaccuracy. In many problem contexts however this trade-off does not hold. Thispaper discusses a regression problem context to predict flight take-off delayswhere the most accurate data regression model was trained via the XGBoostimplementation of gradient boosted decision trees. While building an XGB-CBRTwin and converting the XGBoost feature importance into global weights in theCBR model, the resultant CBR model alone provides the most accurate localprediction, maintains the global importance to provide a global explanation ofthe model, and offers the most interpretable representation for localexplanations. This resultant CBR model becomes a benchmark of accuracy andinterpretability for this problem context, and hence it is used to evaluate thetwo additive feature attribute methods SHAP and LIME to explain the XGBoostregression model. The results with respect to local accuracy and featureattribution lead to potentially valuable future work.

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