How do Decision Trees compare with neural networks in interpretability?

Decision Trees are highly interpretable due to explicit rules, while neural networks are black-box models.

Decision Trees provide human-readable decision paths where each root-to-leaf path can be interpreted as an if-then rule. Neural networks, however, consist of layered nonlinear transformations that are difficult to interpret directly. While techniques like SHAP or LIME can approximate neural network explanations, trees remain inherently more transparent. This makes them preferred in regulated domains like banking and healthcare.