| diversityForest-package {diversityForest} | R Documentation |
Diversity Forests
Description
The diversity forest algorithm is not a specific algorithm, but an alternative candidate split sampling scheme that makes complex split procedures in random forests possible computationally by drastically reducing the numbers of candidate splits that need to be evaluated for each split. It also avoids the well-known variable selection bias in conventional random forests that has the effect that variables with many possible splits are selected too frequently for splitting (Strobl et al., 2007). For details, see Hornung (2022).
Details
This package currently features two types of diversity forests:
the basic form of diversity forests that uses univariable, binary splitting, which is also used in conventional random forests
interaction forests (IFs) (Hornung & Boulesteix, 2022), which use bivariable splitting to model quantitative and qualitative interaction effects. IFs feature the Effect Importance Measure (EIM), which ranks the variable pairs with respect to the predictive importance of their quantitative and qualitative interaction effects. The individual variables can be ranked as well using EIM. For details, see Hornung & Boulesteix (2022).
Diversity forests with univariable splitting can be constructed using the function divfor and
interaction forests using the function interactionfor. Both functions support categorical,
metric, and survival outcomes.
This package is a fork of the R package 'ranger' that implements random forests using an efficient C++ implementation. The documentation is in large parts taken from 'ranger', where some parts of the documentation may not apply to (the current version of) the 'diversityForest' package.
Details on further functionalities of the code that are not presented in the help pages of 'diversityForest' are found in the help pages of 'ranger', version 0.11.0, because 'diversityForest' is based on the latter version of 'ranger'. The code in the example sections can be used as a template for all basic application scenarios with respect to classification, regression and survival prediction.
References
Hornung, R. (2022). Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <doi:10.1007/s42979-021-00920-1>.
Hornung, R., Boulesteix, A.-L. (2022). Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects. Computational Statistics & Data Analysis 171:107460, <doi:10.1016/j.csda.2022.107460>.
Strobl, C., Boulesteix, A.-L., Zeileis, A., Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8:25, <doi:10.1186/1471-2105-8-25>.
Wright, M. N., Ziegler, A. (2017). ranger: A fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software 77:1-17, <doi:10.18637/jss.v077.i01>.