ordinalForest (version 2.0)
Ordinal Forests: Prediction and Variable Ranking with Ordinal
Target Variables
Description
Ordinal forests (OF) are a method for ordinal regression with high-dimensional and low-dimensional
data that is able to predict the values of the ordinal target variable for new observations
based on a training dataset. Using a (permutation-based) variable importance measure it is moreover
possible to rank the covariates with respect to their importances in the prediction of the values
of the ordinal target variable.
OF will be presented in an upcoming technical report by Hornung et al..
The main functions of the package are: ordfor() (construction of OF) and predict.ordfor()
(prediction of the target variable values of new observations).