moreparty

Tools for conditional inference trees and random forests

This package aims at complementing the party and partykit packages with parallelization and interpretation tools.

It provides functions for :

• parallelized conditional random forest
• parallelized variable importance
• feature selection : recursive and non-recursive feature elimination, algorithms based on permutation tests
• accumulated local effects (ALE), partial dependence and interaction strength
• surrogate tree
• prototypes
• getting any tree from a forest
• assessing the stability of a conditional tree
• bivariate association measures
• dot plots for variable importance and effects

It also provides a module and a shiny app for conditional inference trees.

Installation

Execute the following code within R:

if (!require(devtools)){
    install.packages('devtools')
    library(devtools)
}
install_github("nicolas-robette/moreparty")

References

Altmann A., Toloşi L., Sander O., and Lengauer T. “Permutation importance: a corrected feature importance measure”. Bioinformatics, 26(10):1340-1347, 2010.

Apley, D. W., Zhu J. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models”. arXiv:1612.08468v2, 2019.

Gregorutti B., Michel B., and Saint Pierre P. “Correlation and variable importance in random forests”. arXiv:1310.5726, 2017.

Hapfelmeier A. and Ulm K. “A new variable selection approach using random forests”. Computational Statistics and Data Analysis, 60:50–69, 2013.

Hothorn T., Hornik K., Van De Wiel M.A., Zeileis A. “A lego system for conditional inference”. The American Statistician. 60:257–263, 2006.

Hothorn T., Hornik K., Zeileis A. “Unbiased Recursive Partitioning: A Conditional Inference Framework”. Journal of Computational and Graphical Statistics, 15(3):651-674, 2006.

Molnar, C. Interpretable machine learning. A Guide for Making Black Box Models Explainable, 2019. (https://christophm.github.io/interpretable-ml-book/)

Strobl, C., Malley, J., and Tutz, G. “An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests”. Psychological methods, 14(4):323-348, 2009.