partykit: A modular toolkit for recursive partytioning in R

Arbeitspapier

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)

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Papers in Economics and Statistics ; No. 2014-10

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Neural Networks and Related Topics ; Econometric Software
• Thema: recursive partitioning ; regression trees ; classification trees ; statistical learning ; (d) are available in vignettes accompanying the package.

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Hothorn, Torsten ; Zeileis, Achim
• Ereignis: Veröffentlichung
• (wer): University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
• (wo): Innsbruck
• (wann): 2014

Weitere Informationen

• Handle: http://hdl.handle.net/10419/101073
• Letzte Aktualisierung: 12.02.2025, 21:30 MEZ