moreparty
A Toolbox for Conditional Inference Trees and Random Forests
Additions to 'party' and 'partykit' packages : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017)
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.
Documentation
Please visit https://nicolas-robette.frama.io/moreparty/ for documentation
Installation
Execute the following code within R:
if (!require(devtools)){
install.packages('devtools')
library(devtools)
}
install_git("https://framagit.org/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.