Chapter 7 describes approaches for making predictions, including:

• Different types of prediction models
• How to assess the accuracy of the models generated
• Issues when applying models
• Simple regression models
• k-nearest neighbors
• Classification and regression trees
• Neural networks

• Methods for combining models, such as bagging and boosting:
• Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Second Edition (2005) by I.H. Witten and E. Frank
  
  
• Confidence metrics for simple linear regression:
• Complete Idiot’s Guide to Statistics (2004) by R.A. Donnelly
• Multivariate Statistical Analysis: A Conceptual Introduction, (1991) by S.K. Kachigan
• Even You Can Learn Statistics: A guide for everyone who has ever been afraid of statistics (2005) by D.M. Levine and D.F. Stephan
   
  
• Neural Networks:
• Fundamentals of Neural Networks (1994) by L.V. Fausett
  
  
• Multiple linear regression:
• Applied Regression Analysis and Other Multivariate Methods Third Edition (1998) by D.G. Kleinbaum, L. L. Kupper, K. E. Muller, and A. Nizam
  
  
• Logistic regression:
• Categorical Data Analysis (2002) by A. Agresti
   
  
• Random forests:
• Kwok, S and C. Carter, Multiple decision trees, In Schachter, R. D., T. S. Levitt, L. N. Kanal and J. F. Lemer (eds), Artificial Intelligence 4, pp. 327 - 335, Elsevier Science, Amsterdam, 1990
  
  
• Rule-based classifiers:
• Data Mining with SQL Server 2005 (2005) by Z. Tang and J. MacLennan
  
  
• Naïve Bayes:
• Data Mining with SQL Server 2005 (2005) by Z. Tang and J. MacLennan
  
  
• Support vector machines:
• An Introduction to Support Vector Machines and other Kernel-based Learning Methods (2000) by N. Cristianini and J. Shawe-Taylor

• Regression
• k-nearest neighbors
• Classification and regression trees
• Neural networks
• Apply models