  In classification and forecasting with tabular data, one often utilizes
tree-based models. Those can be competitive with deep neural networks on
tabular data and, under some conditions, explainable. The explainability
depends on the depth of the tree and the accuracy in each leaf of the tree. We
point out that decision trees containing leaves with unbalanced accuracy can
provide misleading explanations. Low-accuracy leaves give less valid
explanations, which could be interpreted as unfairness among subgroups
utilizing these explanations. Here, we train a shallow tree with the objective
of minimizing the maximum misclassification error across all leaf nodes. The
shallow tree provides a global explanation, while the overall statistical
performance of the shallow tree can become comparable to state-of-the-art
methods (e.g., well-tuned XGBoost) by extending the leaves with further models.
