Kislay Keshari explains the Naive Bayes algorithm and shows an implementation in Python:
Naive Bayes in the Industry
Now that you have an idea of what exactly Naive Bayes is and how it works, let’s see where it is used in the industry.
RSS Feeds
Our first industrial use case is News Categorization, or we can use the term ‘text classification’ to broaden the spectrum of this algorithm. News on the web is rapidly growing where each news site has its own different layout and categorization for grouping news. Companies use a web crawler to extract useful text from HTML pages of news articles to construct a Full Text RSS. The contents of each news article is tokenized (categorized). In order to achieve better classification results, we remove the less significant words, i.e. stop, from the document. We apply the naive Bayes classifier for classification of news content based on news code.
It’s a good overview of the topic and a particular implementation in Python. Naive Bayes is a technique which you want in the bag: there are a lot of techniques which tend to be better in specific domains, but Naive Bayes is easy to implement and usually provides acceptable performance.