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tree.interpreter  

Random Forest Prediction Decomposition and Feature Importance Measure
View on CRAN: Click here


Download and install tree.interpreter package within the R console
Install from CRAN:
install.packages("tree.interpreter")

Install from Github:
library("remotes")
install_github("cran/tree.interpreter")

Install by package version:
library("remotes")
install_version("tree.interpreter", "0.1.1")



Attach the package and use:
library("tree.interpreter")
Maintained by
Qingyao Sun
[Scholar Profile | Author Map]
First Published: 2019-10-30
Latest Update: 2020-02-05
Description:
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>.
How to cite:
Qingyao Sun (2019). tree.interpreter: Random Forest Prediction Decomposition and Feature Importance Measure. R package version 0.1.1, https://cran.r-project.org/web/packages/tree.interpreter. Accessed 29 Apr. 2025.
Previous versions and publish date:
0.1.0 (2019-10-30 18:40)
Other packages that cited tree.interpreter R package
View tree.interpreter citation profile
Other R packages that tree.interpreter depends, imports, suggests or enhances
Complete documentation for tree.interpreter
Functions, R codes and Examples using the tree.interpreter R package
Some associated functions: MDI . MDIoob . featureContrib . tidyRF . trainsetBias . tree.interpreter . 
Some associated R codes: MDI.R . MDIoob.R . RcppExports.R . featureContrib.R . tidyRF.R . trainsetBias.R . zzz.R .  Full tree.interpreter package functions and examples
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