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- W1551350443 abstract "Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also be formed and used for future prediction. The inTrees framework can applied to both classification and regression problems, and is applicable to many types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the inTrees R package." @default.
- W1551350443 created "2016-06-24" @default.
- W1551350443 creator A5007259870 @default.
- W1551350443 date "2014-08-23" @default.
- W1551350443 modified "2023-10-15" @default.
- W1551350443 title "Interpreting Tree Ensembles with inTrees" @default.
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- W1551350443 doi "https://doi.org/10.48550/arxiv.1408.5456" @default.
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