Provide a comprehensive response describing naive Bayes?
Read chapter 7 of uploaded document and please provide responses
Read chapter 7 of uploaded document and please provide responses to the following items:
(a) Provide a comprehensive response describing naive Bayes?
(b) Explain how naive Bayes is used to filter spam. Please make sure to explain how this process works.
(c) Explain how naive Bayes is used by insurance companies to detect potential fraud in the claim process.
Requirements:
Work must be 2 FULL pages(more than 1000 words), single spaced, 12 font Times New Roman. The cover and reference page must be on separate pages with at least 8 reputable sources.You should have at least one in text citation for every reference and try to use reference other than or in addition to the uploaded textbook. Must contain a properly formatted in-text citation and scholarly reference.
Answer Preview………………..
Naïve Bayes Classifier. The naïve Bayes method is a modification on the Bayes’ theorem. The method uses probabilistic classification. The Bayes theorem relates two events through calculating their conditional probabilities. This theorem is applied in classification as naïve Bayes. An assumption made in the classification is that a feature of the class is independent of other features present in the class. The algorithm uses this assumption even when the features are dependent. The algorithm operates using categorical variables. Continuous variables can also be used in the algorithm only after they converted to categorical variables through discretization (Bird 2015). The easy to implement without historical information trait of this algorithm is what makes it more eligible in the classification of text documents. This leads to the most common applications of spam filtering and fraud detection.Naïve Bayes Classifier. The naïve Bayes method is a modification on the Bayes’ theorem. The method uses probabilistic classification. The Bayes theorem relates two events through calculating their conditional probabilities. This theorem is applied in classification as naïve Bayes. An assumption made in the classification is that a feature of the class is independent of other features present in the class. The algorithm uses this assumption even when the features are dependent. The algorithm operates using categorical variables. Continuous variables can also be used in the algorithm only after they converted to categorical variables through discretization (Bird 2015). The easy to implement without historical information trait of this algorithm is what makes it more eligible in the classification of text documents. This leads to the most common applications of spam filtering and fraud detection……………….
APA 1180