Prediction and Interpretation in Decision Trees for Classification and Regression
Check for forbidden split results in trees
Nested PrInDT with additional undersampling of a factor with two unb...
Posterior analysis of conditional inference trees: distribution of a s...
The basic undersampling loop for classification
Conditional inference tree (ctree) based on all observations
Conditional inference trees (ctrees) based on consecutive parts of the...
Multiple label classification based on resampling by PrInDT
Multiple label classification based on all observations
PrInDT analysis for a classification problem with multiple classes.
Conditional inference tree (ctree) for multiple classes on all observa...
Regression tree resampling by the PrInDT method
Regression tree based on all observations
Repeated PrInDT for specified percentage combinations
Optimization of conditional inference trees from the package 'party' for classification and regression. 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 (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. References are: -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" <arXiv:2103.02336>; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" <arXiv:2103.14931>; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" <arXiv:2108.05129>.