diversityForest: Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling

Implementation of three methods based on the diversity forest (DF) algorithm (Hornung, 2022, <doi:10.1007/s42979-021-00920-1>), a split-finding approach that enables complex split procedures in random forests. The package includes: 1. Interaction forests (IFs) (Hornung & Boulesteix, 2022, <doi:10.1016/j.csda.2022.107460>): Model quantitative and qualitative interaction effects using bivariable splitting. Come with the Effect Importance Measure (EIM), which can be used to identify variable pairs that have well-interpretable quantitative and qualitative interaction effects with high predictive relevance. 2. Two random forest-based variable importance measures (VIMs) for multi-class outcomes: the class-focused VIM, which ranks covariates by their ability to distinguish individual outcome classes from the others, and the discriminatory VIM, which measures overall covariate influence irrespective of class-specific relevance. 3. The basic form of diversity forests that uses conventional univariable, binary splitting (Hornung, 2022). Except for the multi-class VIMs, all methods support categorical, metric, and survival outcomes. The package includes visualization tools for interpreting the identified covariate effects. Built as a fork of the 'ranger' R package (main author: Marvin N. Wright), which implements random forests using an efficient C++ implementation.

Package details

AuthorRoman Hornung [aut, cre], Marvin N. Wright [ctb, cph]
MaintainerRoman Hornung <hornung@ibe.med.uni-muenchen.de>
LicenseGPL-3
Version0.6.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("diversityForest")

Try the diversityForest package in your browser

Any scripts or data that you put into this service are public.

diversityForest documentation built on June 8, 2025, 1:23 p.m.