Factors That Can Affect Model Performance

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 20 in Applied Predictive Modeling, 2013, pp 521-546 from Springer

Abstract: Abstract Several of the preceding chapters have focused on technical pitfalls of predictive models, such as over-fitting and class imbalances. Often, true success may depend on aspects of the problem that are not directly related to the model itself. This chapter discusses topics such as: Type III errors (answering the wrong question, Section 20.1), the effect of unwanted noise in the response (Section 20.2) and in the predictors (Section 20.3), the impact of discretizing continuous outcomes (Section 20.4), extrapolation (Section 20.5), and the impact of a large number of samples (Section 20.6). In the Computing Section (20.7) we illustrate the implementation of an algorithm for determining samples’ similarity to the training set. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Keywords: Random Forest; Linear Regression Model; Solubility Data; Multivariate Adaptive Regression Spline; Measurement System Error (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_20

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

DOI: 10.1007/978-1-4614-6849-3_20

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 ().