August 2014
Beginner to intermediate
304 pages
7h 10m
English
Content preview from Python 3 Text Processing with NLTK 3 Cookbook - Second EditionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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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