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Machine Learning in Action by Peter Harrington

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Chapter 4. Classifying with probability theory: naïve Bayes

 

This chapter covers
  • Using probability distributions for classification
  • Learning the naïve Bayes classifier
  • Parsing data from RSS feeds
  • Using naïve Bayes to reveal regional attitudes

 

In the first two chapters we asked our classifier to make hard decisions. We asked for a definite answer for the question “Which class does this data instance belong to?” Sometimes the classifier got the answer wrong. We could instead ask the classifier to give us a best guess about the class and assign a probability estimate to that best guess.

Probability theory forms the basis for many machine-learning algorithms, so it’s important that you get a good grasp on this topic. We touched on probability ...

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