bayes takes a document (piece of text), and tells you what category that document belongs to.
You can use this for categorizing any text content into any arbitrary set of categories. For example:
npm install bayes
var bayes =var classifier =// teach it positive phrasesclassifierclassifier// teach it a negative phraseclassifier// now ask it to categorize a document it has never seen beforeclassifier// => 'positive'// serialize the classifier's state as a JSON string.var stateJson = classifier// load the classifier back from its JSON representation.var revivedClassifier = bayes
Returns an instance of a Naive-Bayes Classifier.
Pass in an optional
options object to configure the instance. If you specify a
tokenizer function in
options, it will be used as the instance's tokenizer. It receives a (string)
text argument - this is the string value that is passed in by you when you call
.categorize(). It must return an array of tokens. The default tokenizer removes punctuation and splits on spaces.
var classifier =
Teach your classifier what
text belongs to. The more you teach your classifier, the more reliable it becomes. It will use what it has learned to identify new documents that it hasn't seen before.
category it thinks
text belongs to. Its judgement is based on what you have taught it with .learn().
Returns the JSON representation of a classifier.
Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from
(The MIT License)
Copyright (c) by Tolga Tezel email@example.com
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