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:
tree.interpreter_0.1.1.tar.gz
tree.interpreter_0.1.1.zip(r-4.5)tree.interpreter_0.1.1.zip(r-4.4)tree.interpreter_0.1.1.zip(r-4.3)
tree.interpreter_0.1.1.tgz(r-4.4-x86_64)tree.interpreter_0.1.1.tgz(r-4.4-arm64)tree.interpreter_0.1.1.tgz(r-4.3-x86_64)tree.interpreter_0.1.1.tgz(r-4.3-arm64)
tree.interpreter_0.1.1.tar.gz(r-4.5-noble)tree.interpreter_0.1.1.tar.gz(r-4.4-noble)
tree.interpreter_0.1.1.tgz(r-4.4-emscripten)tree.interpreter_0.1.1.tgz(r-4.3-emscripten)
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')) |
Bug tracker:https://github.com/nalzok/tree.interpreter/issues
data-sciencedatascienceinterpretabilitymachine-learningrandom-forest
Last updated 5 years agofrom:0a04a7a790. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win-x86_64 | NOTE | Nov 21 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 21 2024 |
R-4.4-win-x86_64 | NOTE | Nov 21 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 21 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 21 2024 |
R-4.3-win-x86_64 | NOTE | Nov 21 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 21 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 21 2024 |
Exports:featureContribfeatureContribTreeMDIMDIoobMDIoobTreeMDITreetidyRFtrainsetBiastrainsetBiasTree
Dependencies:RcppRcppArmadillo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Feature Contribution | featureContrib featureContribTree |
Debiased Mean Decrease in Impurity | MDIoob MDIoobTree |
Mean Decrease in Impurity | MDI MDITree |
Tidy Random Forest | tidyRF |
Trainset Bias | trainsetBias trainsetBiasTree |
Random Forest Prediction Decomposition and Feature Importance Measure | tree.interpreter |