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Python 3 Text Processing with NLTK 3 Cookbook - Second Edition
book

Python 3 Text Processing with NLTK 3 Cookbook - Second Edition

by Jacob Perkins
August 2014
Beginner to intermediate content levelBeginner to intermediate
304 pages
7h 10m
English
Packt Publishing
Content preview from Python 3 Text Processing with NLTK 3 Cookbook - Second Edition

Training a Naive Bayes classifier

Now that we can extract features from text, we can train a classifier. The easiest classifier to get started with is the NaiveBayesClassifier class. It uses the Bayes theorem to predict the probability that a given feature set belongs to a particular label. The formula is:

P(label | features) = P(label) * P(features | label) / P(features)

The following list describes the various parameters from the previous formula:

  • P(label): This is the prior probability of the label occurring, which is the likelihood that a random feature set will have the label. This is based on the number of training instances with the label compared to the total number of training instances. For example, if 60/100 training instances have the ...
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Publisher Resources

ISBN: 9781782167853Supplemental Content