Improving Stability in Decision Tree Models

Understand

Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential.

• Recent work has focused on improving various aspects of decision trees, including their predictive power and robustness; however, their instability, albeit well-documented, has been addressed to a lesser extent.
• In this paper, we take a step towards the stabilization of decision tree models through the lens of real-world health care applications due to the relevance of stability and interpretability in this space.
• We introduce a new distance metric for decision trees and use it to determine a tree's level of stability.