Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/101073
Authors: 
Hothorn, Torsten
Zeileis, Achim
Year of Publication: 
2014
Series/Report no.: 
Working Papers in Economics and Statistics 2014-10
Abstract: 
The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree-structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that unified print/plot/predict methods are available; (b) dedicated methods for trees with constant fits in the leaves (or terminal nodes) along with suitable coercion functions to create such trees (e.g., by rpart, RWeka, PMML); (c) a reimplementation of conditional inference trees (ctree, originally provided in the party package); (d) an extended reimplementation of model-based recursive partitioning (mob, also originally in party) along with dedicated methods for trees with parametric models in the leaves. Here, a brief overview of the package and its design is given while more detailed discussions of items (a)
Subjects: 
recursive partitioning
regression trees
classification trees
statistical learning
R
(d) are available in vignettes accompanying the package.
JEL: 
C14
C45
C87
Document Type: 
Working Paper

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