A Short Tour of the Predictive Modeling Process

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 2 in Applied Predictive Modeling, 2013, pp 19-26 from Springer

Abstract: Abstract To begin Part I of this work, we present a simple example that illustrates the broad concepts of model building. Section 2.1 provides an overview of a fuel economy data set for which the objective is to predict vehicles' fuel economy based on standard vehicle predictors such as engine displacement, number of cylinders, type of transmission, and manufacturer. In the context of this example, we explain the concepts of “spending” data, estimating model performance, building candidate models, and selecting the optimal model (Section 2.2).

Keywords: Predictive Process Models; Fuel Economy; Engine Displacement; Estimate Model Performance; MARS Model (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_2

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

DOI: 10.1007/978-1-4614-6849-3_2

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 ().