Abstract
Every day people are faced with questions such as “What route should I take to work today?” “Should I switch to a different cell phone carrier?” “How should I invest my money?” or “Will I get cancer?” These questions indicate our desire to know future events, and we earnestly want to make the best decisions towards that future. In this chapter we explore the contrast between the competing modeling objectives of prediction and interpretation (Section 1.1), outline the foundational components for developing predictive models (Section 1.2) and define common terminology (Section 1.3), and provide summaries of data sets that will be used throughout the book (Section 1.4). The chapter ends with an overview of the four parts of the book (Section 1.5), and notation used throughout the text (Section 1.6).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
This type of sampling is very similar to case-control studies in the medical field.
References
Ayres I (2007). Super Crunchers: Why Thinking–By–Numbers Is The New Way To Be Smart. Bantam.
Duhigg C (2012). “How Companies Learn Your Secrets.” The New York Times. URL http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.
Fanning K, Cogger K (1998). “Neural Network Detection of Management Fraud Using Published Financial Data.” International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1), 21–41.
Geisser S (1993). Predictive Inference: An Introduction. Chapman and Hall.
Kansy M, Senner F, Gubernator K (1998). “Physiochemical High Throughput Screening: Parallel Artificial Membrane Permeation Assay in the Description of Passive Absorption Processes.” Journal of Medicinal Chemistry, 41, 1007–1010.
Levy S (2010). “The AI Revolution is On.” Wired.
Maindonald J, Braun J (2007). Data Analysis and Graphics Using R. Cambridge University Press, 2nd edition.
Muenchen R (2009). R for SAS and SPSS Users. Springer.
R Development Core Team (2010). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Rodriguez M (2011). “The Failure of Predictive Modeling and Why We Follow the Herd.” Technical report, Concepcion, Martinez & Bellido.
Shachtman N (2011). “Pentagon’s Prediction Software Didn’t Spot Egypt Unrest.” Wired.
Spector P (2008). Data Manipulation with R. Springer.
US Commodity Futures Trading Commission and US Securities & Exchange Commission (2010). Findings Regarding the Market Events of May 6, 2010.
Venables W, Smith D, the R Development Core Team (2003). An Introduction to R. R Foundation for Statistical Computing, Vienna, Austria, version 1.6.2 edition. ISBN 3-901167-55-2, URL http://www.R-project.org.
Verzani J (2002). “simpleR – Using R for Introductory Statistics.” URL http://www.math.csi.cuny.edu/Statistics/R/simpleR.
Westphal C (2008). Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies. CRC Press.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Kuhn, M., Johnson, K. (2013). Introduction. In: Applied Predictive Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6849-3_1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6849-3_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6848-6
Online ISBN: 978-1-4614-6849-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)