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) .


Reference manual

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0.1.1 by Qingyao Sun, a year ago


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