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) .


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install.packages("tree.interpreter")

0.1.0 by Qingyao Sun, 18 days ago


https://github.com/nalzok/tree.interpreter


Report a bug at https://github.com/nalzok/tree.interpreter/issues


Browse source code at https://github.com/cran/tree.interpreter


Authors: Qingyao Sun


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports Rcpp

Suggests MASS, randomForest, ranger, testthat, knitr, rmarkdown, covr

Linking to Rcpp, RcppArmadillo


See at CRAN