Naïve Bayes Estimation and Bayesian Networks
Daniel T. Larose · 2005
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.