期刊
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
卷 7, 期 4, 页码 277-287出版社
SPRINGERNATURE
DOI: 10.1007/s41060-018-0144-8
关键词
Decision tree; Rule extraction; Rule-based learner; Random forest; Boosted trees
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.
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