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Erschienen in:
2016 | OriginalPaper | Buchkapitel
10. The Multinomial Naïve Bayes Prediction Function
verfasst von : Brian Steele, John Chandler, Swarna Reddy
Erschienen in: Algorithms for Data Science
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Abstract
The naïve Bayes prediction function is a computationally and conceptually simple algorithm. While the performance of the algorithm generally is not best among competitors when the predictor variables are quantitative, it does well with categorical predictor variables, and it’s especially well-suited for categorical predictor variables with many categories. In this chapter we develop the multinomial naïve Bayes prediction function, the incarnation of naïve Bayes for categorical predictors. We develop the function from its mathematical foundation before applying it to two very different problems: predicting the authorship of the Federalist Papers and a problem from the business marketing domain—classifying shoppers based on their grocery store purchases. The Federalist Papers application provides the opportunity to work with textual data.