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Pros:

  • The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy.
  • It works well with high-dimensional data such as text classification, email spam detection.

Cons:

  • The assumption that all features are independent is not usually the case in real life so it makes naive bayes algorithm less accurate than complicated algorithms. Speed comes at a cost!
Learn Naive Bayes Algorithm | Naive Bayes Classifier Examples
Naive Bayes Algorithm is a machine learning classification algorithm. Learn to implement a Naive Bayes classifier in Python and R with examples.
https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/

#Pros #Cons #NaiveBayes #MachineLearning #Probyto #ProbytoAI

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