Innovative Complex Split Procedures in Random Forests Through
Candidate Split Sampling
Implements interaction forests [1], which are specific diversity forests and
the basic form of diversity forests that uses univariable, binary splitting [2].
Interaction forests (IFs) are ensembles of decision trees that model quantitative and
qualitative interaction effects using bivariable splitting. IFs come with the
Effect Importance Measure (EIM), which can be used to identify variable pairs that
feature quantitative and qualitative interaction effects with high predictive
relevance. IFs and EIM focus on well interpretable forms of interactions.
The package also offers plot functions for visualising the estimated forms of
interaction effects.
Categorical, metric, and survival outcomes are supported.
This is a fork of the R package 'ranger' (main author: Marvin N. Wright) that
implements random forests using an efficient C++  implementation.
References:
[1] Hornung, R. & Boulesteix, A.-L. (2021) Interaction Forests: Identifying and
exploiting interpretable quantitative and qualitative interaction effects.
Technical Report No. 237, Department of Statistics, University of Munich,
<doi:10.5282/ubm/epub.75269>.
[2] 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>.

Implements interaction forests [1], which are specific diversity forests and
the basic form of diversity forests that uses univariable, binary splitting [2].
Interaction forests (IFs) are ensembles of decision trees that model quantitative and
qualitative interaction effects using bivariable splitting. IFs come with the
Effect Importance Measure (EIM), which can be used to identify variable pairs that
feature quantitative and qualitative interaction effects with high predictive
relevance. IFs and EIM focus on well interpretable forms of interactions.
The package also offers plot functions for visualising the estimated forms of
interaction effects.
Categorical, metric, and survival outcomes are supported.
This is a fork of the R package 'ranger' (main author: Marvin N. Wright) that
implements random forests using an efficient C++  implementation.
References:
[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>.
[2] 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>.