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tree.interpreter

An R re-implementation of the treeinterpreter package on PyPI. 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>.

Installation

To install the CRAN version, run

install.packages('tree.interpreter')

To install the latest development version, run

devtools::install_github('nalzok/tree.interpreter')

macOS users might want to follow the set up instructions by The Coatless Professor to minimize operational headaches and maximize computational performance.

Usage

For example, you can calculate the state-of-the-art MDI-oob feature importance measure for ranger. See vignette('MDI', package='tree.interpreter') for more information.

library(ranger)
library(tree.interpreter)

set.seed(42L)
rfobj <- ranger(mpg ~ ., mtcars, keep.inbag = TRUE)
tidy.RF <- tidyRF(rfobj, mtcars[, -1], mtcars[, 1])
mtcars.MDIoob <- MDIoob(tidy.RF, mtcars[, -1], mtcars[, 1])
mtcars.MDIoob

References

This package companies the paper A Debiased MDI Feature Importance Measure for Random Forests.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.