Regression Trees and Rule-Based Models

Max Kuhn and Kjell Johnson
Additional contact information
Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 8 in Applied Predictive Modeling, 2013, pp 173-220 from Springer

Abstract: Abstract Tree-based models consist of one or more nested if-then statements for the predictors that partition the data. Within these partitions, a model is used to predict the outcome. Regression trees and regression model trees are basic partitioning models and are covered in Sections 8.1 and 8.2, respectively. In Section 8.3, we present rule-based models, which are models governed by if-then conditions (possibly created by a tree) that have been collapsed into independent conditions. Rules can be simplified or pruned in a way that samples are covered by multiple rules. Ensemble methods combine many trees (or rule-based models) into one model and tend to have much better predictive performance than single tree- or rule-based model. Popular ensemble techniques are bagging (Section 8.4), random forests (Section 8.5), boosting (Section 8.6), and Cubist (Section 8.7). In the Computing Section (8.8), we demonstrate how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Keywords: Random Forest; Regression Tree; Terminal Node; Solubility Data; Weak Learner (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6849-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9781461468493

DOI: 10.1007/978-1-4614-6849-3_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().