Random Forestlike tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing OneHot encoding in the processing). Can also be used to create a regular (nonstratified) Random Forestlike model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression).
Package details 


Author  David Cortes <david.cortes.rivera@gmail.com> 
Maintainer  David Cortes <david.cortes.rivera@gmail.com> 
License  GPL3 
Version  0.2.2 
Package repository  View on CRAN 
Installation 
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