
Chapter 7: Prediction 

Description 
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
 knearest neighbors
 Classification and regression trees
 Neural networks

Further Reading 
 Methods for combining models, such as bagging and boosting:
 Confidence metrics for simple linear regression:
 Neural Networks:
 Multiple linear regression:
 Logistic regression:
 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
 Rulebased classifiers:
 Naïve Bayes:
 Support vector machines:

Tutorials 


Chapter 8: Deployment 
