Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Comp Stat

Recursive partitioning

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Recursive partitioning (RP) is a predictive approach with minimal statistical or model assumptions, which models the relationship in terms of trees or dendrograms. It is particularly appropriate for initial exploration of large data sets, especially messy ones, and may either validate other approaches with stronger model assumptions or lead to a final analysis in its own right. It may be used for either a nominal scale (categorical) or an interval‐scale (numeric) dependent variable. A major issue is the size of tree to be fitted; different approaches for this have been proposed. Like many other feature selection methods, RP is unstable, the model being potentially sensitive to minor perturbations in the data. Fitting multiple trees helps explore alternative models, and also provides better predictions than those giving by a single tree. RP is well supported in both commercial and public domain software. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering

Dendrogram of MAOI activity of compounds.

[ Normal View | Magnified View ]

Classification of diabetics.

[ Normal View | Magnified View ]

Related Articles

Tree‐structured classifiers
Algorithms for Chemoinformatics: an Interdisciplinary View

Browse by Topic

Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts