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

An R re-implementation of the 'treeinterpreter' package on PyPI <https://pypi.org/project/treeinterpreter/>. 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: 0.1.0
Imports: Rcpp (≥ 1.0.2)
LinkingTo: Rcpp, RcppArmadillo
Suggests: MASS, randomForest, ranger, testthat (≥ 2.1.0), knitr, rmarkdown, covr
Published: 2019-10-30
Author: Qingyao Sun
Maintainer: Qingyao Sun <sunqingyao19970825 at gmail.com>
BugReports: https://github.com/nalzok/tree.interpreter/issues
License: MIT + file LICENSE
URL: https://github.com/nalzok/tree.interpreter
NeedsCompilation: yes
Citation: tree.interpreter citation info
Materials: README
CRAN checks: tree.interpreter results

Downloads:

Reference manual: tree.interpreter.pdf
Vignettes: MDI
Package source: tree.interpreter_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
OS X binaries: r-release: not available, r-oldrel: not available

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