On Explaining Decision Trees
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI-explanation, i.e. a subset-minimal set of feature values that entails the prediction. As a result, the paper proposes a novel model for computing PI-explanations of DTs, which enables computing one PI-explanation in polynomial time. Moreover, it is shown that enumeration of PI-explanations can be reduced to the enumeration of minimal hitting sets. Experimental results were obtained on a wide range of publicly available datasets with well-known DT-learning tools, and confirm that in most cases DTs have paths that are proper supersets of PI-explanations.
Code (0)
등록된 구현이 없습니다.
Tasks
Interpretable Machine LearningSimilar Papers 제목 키워드 기반
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to several factors, one being the complexity …
Deep Reinforcement LearningExplainable ModelsImitation Learningreinforcement-learning+3Explaining Kernel Clustering via Decision Trees
Despite the growing popularity of explainable and interpretable machine learning, there is still surprisingly limited work on inherently interpretable clustering methods. Recently, there has been a surge of interest in e…
ClusteringInterpretable Machine LearningTE2Rules: Explaining Tree Ensembles using Rules
Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introdu…
Binary ClassificationDecision MakingExplainable artificial intelligenceExplanation GenerationComparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values
In this work the decision trees are used for explanation of support vector regression model. The decision trees act as a global technique as well as a local technique. They are compared against the popular technique of L…
regressionExplaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees
The attention mechanism has largely improved the performance of end-to-end speech recognition systems. However, the underlying behaviours of attention is not yet clearer. In this study, we use decision trees to explain h…
Decoderspeech-recognitionSpeech Recognition