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Description

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) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions. Also modules and a shiny app for conditional inference trees.

moreparty

Tools for conditional inference trees and random forests

R build status

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.

Metadata

Version

0.4

License

Unknown

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