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On Tackling Explanation Redundancy in Decision Trees
[article]
2022
arXiv
pre-print
Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals for applying interpretable ML models in high-risk applications. The belief in DT interpretability is justified by the fact that explanations for DT predictions are generally expected to be succinct. Indeed, in the case of DTs, explanations
arXiv:2205.09971v1
fatcat:euhzw4y3bndnziymr6onfaw24u