R: Prediction and Interpretation in Decision Trees for Classification and Regression

# Prediction and Interpretation in Decision Trees for Classification and Regression

## Documentation for package ‘PrInDT’ version 2.0.2

- DESCRIPTION file

## Help Pages

C2SPrInDT | Two-stage estimation for classification
data_land | Landscape analysis
data_speaker | Subject pronouns and a predictor with one very frequent level
data_vowel | Vowel length
data_zero | Subject pronouns
Mix2SPrInDT | Two-stage estimation for classification-regression mixtures
NesPrInDT | Nested 'PrInDT' with additional undersampling of a factor with two unbalanced levels
OptPrInDT | Optimisation of undersampling percentages for classification
participant_zero | Participants of subject pronoun study
PostPrInDT | Posterior analysis of conditional inference trees: distribution of a specified variable in the terminal nodes.
PrInDT | The basic undersampling loop for classification
PrInDTAll | Conditional inference tree (ctree) based on all observations
PrInDTAllparts | Conditional inference trees (ctrees) based on consecutive parts of the full sample
PrInDTCstruc | Structured subsampling for classification
PrInDTMulab | Multiple label classification based on resampling by 'PrInDT'
PrInDTMulabAll | Multiple label classification based on all observations
PrInDTMulev | PrInDT analysis for a classification problem with multiple classes.
PrInDTMulevAll | Conditional inference tree (ctree) for multiple classes on all observations
PrInDTreg | Regression tree resampling by the PrInDT method
PrInDTregAll | Regression tree based on all observations
PrInDTRstruc | Structured subsampling for regression
R2SPrInDT | Two-stage estimation for regression
RePrInDT | Repeated 'PrInDT' for specified percentage combinations
SimCPrInDT | Interdependent estimation for classification
SimMixPrInDT | Interdependent estimation for classification-regression mixtures
SimRPrInDT | Interdependent estimation for regression