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) <doi:10.48550/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 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 NEWS
CRAN checks:	tree.interpreter results

Documentation:

Reference manual:	tree.interpreter.pdf
Vignettes:	MDI

Downloads:

Package source:	tree.interpreter_0.1.1.tar.gz
Windows binaries:	r-devel: tree.interpreter_0.1.1.zip, r-release: tree.interpreter_0.1.1.zip, r-oldrel: tree.interpreter_0.1.1.zip
macOS binaries:	r-release (arm64): tree.interpreter_0.1.1.tgz, r-oldrel (arm64): tree.interpreter_0.1.1.tgz, r-release (x86_64): tree.interpreter_0.1.1.tgz, r-oldrel (x86_64): tree.interpreter_0.1.1.tgz
Old sources:	tree.interpreter archive

Linking:

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