Package: tree.interpreter 0.1.1

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>.

Authors:Qingyao Sun

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tree.interpreter.pdf |tree.interpreter.html
tree.interpreter/json (API)
NEWS

# Install 'tree.interpreter' in R:
install.packages('tree.interpreter', repos = c('https://nalzok.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/nalzok/tree.interpreter/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

data-sciencedatascienceinterpretabilitymachine-learningrandom-forest

5.87 score 12 stars 6 scripts 1.2k downloads 53 mentions 9 exports 2 dependencies

Last updated 5 years agofrom:0a04a7a790. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-win-x86_64NOTENov 21 2024
R-4.5-linux-x86_64NOTENov 21 2024
R-4.4-win-x86_64NOTENov 21 2024
R-4.4-mac-x86_64NOTENov 21 2024
R-4.4-mac-aarch64NOTENov 21 2024
R-4.3-win-x86_64NOTENov 21 2024
R-4.3-mac-x86_64NOTENov 21 2024
R-4.3-mac-aarch64NOTENov 21 2024

Exports:featureContribfeatureContribTreeMDIMDIoobMDIoobTreeMDITreetidyRFtrainsetBiastrainsetBiasTree

Dependencies:RcppRcppArmadillo

Calculating MDI and MDI-oob with tree.interpreter

Rendered fromMDI.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2019-10-21
Started: 2019-10-20