When a CBR in Hand is Better than Twins in the Bush
Mobyen Uddin Ahmed Shaibal Barua Shahina Begum Mir Riyanul Islam Rosina O Weber
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 does not hold. This
paper discusses a regression problem context to predict flight take-off delays
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 XGBoost feature importance into global weights in the
CBR model, 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 becomes a benchmark of accuracy and
interpretability for this problem context, and hence it is used to evaluate the
two additive feature attribute methods SHAP and LIME to explain the XGBoost
regression model. The results with respect to local accuracy and feature
attribution lead to potentially valuable future work.