Interpreting tree ensembles with inTrees

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand. In this work, we provide the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions. T...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of data science and analytics Vol. 7; no. 4; pp. 277 - 287
Main Author Deng, Houtao
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.06.2019
Subjects
Artificial Intelligence
Business Information Systems
Computational Biology/Bioinformatics
Computer Science
Data Mining and Knowledge Discovery
Database Management
Regular Paper
Rule-based learner
Random forest
Decision tree
Boosted trees
Rule extraction
Online AccessGet full text

Cover

More Information
Summary:Tree ensembles such as random forests and boosted trees are accurate but difficult to understand. In this work, we provide the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions. The inTrees framework can be applied to multiple types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the “inTrees” R package.
ISSN:2364-415X
2364-4168
DOI:10.1007/s41060-018-0144-8