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Pros and Cons of Naive Bayes Algorithm

Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set.
Pros and Cons of Naive Bayes Algorithm

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/

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