Naive bayes is a bayesian model often used for text classification. Multinomial Naive Bayes specifically classifies observations based on variables with a multinomial distribution (a.k.a. numbers).
Below is a simple text classification using Multinomial Naive Bayes in Lurn.
- Start with some text documents
documents = [
'ruby is a great programming language',
'the giants recently won the world series',
'java is a compiled programming language',
'the jets are a football team'
]
labels = ['computers','sports','computers','sports']- Convert them to arrays of booleans representing which words they contain (or don't contain). Lurn provides vectorizers for this purpose.
vectorizer = Lurn::Text::WordCountVectorizer.new
vectorizer.fit(documents)
vectors = vectorizer.transform(documents)
- Initialize and train the model
model = Lurn::NaiveBayes::MultinomialNaiveBayes.new
model.fit(vectors, labels)
- Classify a new document
new_vectors = vectorizer.transform(['programming is fun'])
# get the most probable class for the new document given the training data
model.max_class(new_vectors.first)
# get the probability score for the most probable class
model.max_probability(new_vectors.first)