tree.interpreter: Random Forest Prediction Decomposition and Feature Importance Measure

An R re-implementation of the 'treeinterpreter' package on PyPI <>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.

Version: 0.1.1
Imports: Rcpp (≥ 1.0.2)
LinkingTo: Rcpp, RcppArmadillo
Suggests: MASS, randomForest, ranger, testthat (≥ 2.1.0), knitr, rmarkdown, covr
Published: 2020-02-05
Author: Qingyao Sun
Maintainer: Qingyao Sun <sunqingyao19970825 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: tree.interpreter citation info
Materials: README NEWS
CRAN checks: tree.interpreter results


Reference manual: tree.interpreter.pdf
Vignettes: MDI
Package source: tree.interpreter_0.1.1.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: tree.interpreter_0.1.1.tgz, r-release: tree.interpreter_0.1.1.tgz, r-oldrel: tree.interpreter_0.1.1.tgz
Old sources: tree.interpreter archive


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