PrInDT
PrInDT is an R-package which was designed by Prof. Dr. Claus Weihs and Prof. Dr. Sarah Buschfeld at the Department for Language, Literature and Culture.
You can find the improved version (1.0.1 with patches) via the green link below.
How to cite:
Claus Weihs, Sarah Buschfeld (2023): PrInDT: Prediction and Interpretation in Decision Trees for Classification and Regression, R package version 1.0, url = {https://CRAN.R-project.org/package=PrInDT} .
Description
PrInDT is an R-package for the optimization of conditional inference trees (ctrees) for classification and regression, developed for linguistic applications. For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results. With the PrInDT package, both the predictive power and the interpretability of ctrees are increased. The performance of ensembles and individual trees is compared.
The package covers the optimization of ctrees for 2-level, multilevel, and multilabel classifications as well as for regression. Subsampling percentages can be varied for the classes in classification and for observations and predictors in regression. Furthermore, the posterior distribution of a specified variable in the terminal nodes of a given tree can be analyzed.
Current PrInDT version
You can find the current PrInDT version and the documentation via the CRAN repository.
Related publications
Weihs, C., Buschfeld, S. (2021a). "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example", Online: arXiv:2103.02336
Weihs, C., Buschfeld, S. (2021b). "NesPrInDT: Nested undersampling in PrInDT", Online: arXiv:2103.14931
Weihs, C., Buschfeld, S. (2021c). "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles", Online: arXiv:2108.05129