diversityForest

Implementations of three diversity forest (DF) (Hornung, 2022, ) variants. The DF algorithm is a split-finding approach that allows complex split procedures to be realized in random forest variants. The three DF variants implemented are: 1. interaction forests (IFs) (Hornung & Boulesteix, 2022, ): 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. multi forests (MuFs) (Hornung & Hapfelmeier, 2024, ): Model multi-class outcomes using multi-way and binary splitting. Come with two variable importance measures (VIMs): The multi-class VIM measures the degree to which the variables are specifically associated with one or more outcome classes, and the discriminatory VIM, similar to conventional VIMs, measures the overall influence strength of the variables. 3. the basic form of diversity forests that uses conventional univariable, binary splitting (Hornung, 2022). Except for multi forests, which are tailored for multi-class outcomes, all included diversity forest variants support categorical, metric, and survival outcomes. The package also includes plotting functions that make it possible to learn about the forms of the effects identified using IFs and MuFs. This is a fork of the R package 'ranger' (main author: Marvin N. Wright), which implements random forests using an efficient C++ implementation.

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