Chapter 5. Naïve Bayes Estimation and Bayesian Networks

  1. Daniel T. Larose Ph.D. Director

Published Online: 30 JAN 2006

DOI: 10.1002/0471756482.ch5

Data Mining Methods and Models

Data Mining Methods and Models

How to Cite

Larose, D. T. (2006) Naïve Bayes Estimation and Bayesian Networks, in Data Mining Methods and Models, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471756482.ch5

Author Information

  1. Department of Mathematical Sciences, Central Connecticut State University, USA

Publication History

  1. Published Online: 30 JAN 2006
  2. Published Print: 11 NOV 2005

ISBN Information

Print ISBN: 9780471666561

Online ISBN: 9780471756484

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Keywords:

  • Bayesian approach;
  • maximum a posteriori classification;
  • odds ratio;
  • posterior odds ratio;
  • balancing the data;
  • Naïve Bayes classification;
  • Bayesian belief networks;
  • WEKA

Summary

Chapter Five begins by contrasting the Bayesian approach with the usual (frequentist) approach to probability. The maximum a posteriori (MAP) classification is defined, which is used to select the preferred response classification. Odds ratios are discussed, including the posterior odds ratio. The importance of balancing the data is discussed. Naïve Bayes classification is derived, using a simplifying assumption which greatly reduces the search space. Methods for handling numeric predictors for naïve Bayes classification are demonstrated. An example of using WEKA for naïve Bayes is provided. Then, Bayesian belief networks (Bayes Nets) are introduced and defined. Methods for using the Bayesian network to find probabilities are discussed. Finally, an example of using Bayes nets in WEKA is provided.