April 2012
Intermediate to advanced
384 pages
11h 19m
English
Content preview from Machine Learning in Action
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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 ...
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