Measuring Performance in Regression 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 5 in Applied Predictive Modeling, 2013, pp 95-100 from Springer

Abstract: Abstract When predicting a numeric outcome, some measure of accuracy is typically used to evaluate the model’s effectiveness. However, there are different ways to measure accuracy, each with its own nuance. In Section 5.1 we define common measures for evaluating quantitative performance. We also discuss the concept of variance-bias trade-off (Section 5.2), and the implication of this principle for predictive modeling. In Section 5.3, we demonstrate how measures of predictive performance can be generated in R.

Keywords: Resid Ual Value; Root Mean Square Error (RMSE); Caret Package; Irreducible Noise; High Bias (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_5

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

DOI: 10.1007/978-1-4614-6849-3_5

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 ().