Making Tree Ensembles Interpretable

2016/06/17 by Satoshi Hara, Kohei Hayashi, Hara, Satoshi +1 · 3 citations
Computer Science · #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1606.05390

openalex publication_date 2016/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human. To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the complex ensemble. A synthetic experiment showed that a complicated tree ensemble was approximated reasonably as interpretable.

Cited by

• Scalable Rule-Based Representation Learning for Interpretable Classification
• Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
• Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery

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