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  • Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data.
  • Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems.
  • It uses the Bayes theorem of probability to predict the class.

Advantages and Disadvantages

  • Advantages:
    This algorithm requires a small amount of training data to estimate the necessary parameters. Naive Bayes classifiers are extremely fast compared to more sophisticated methods.
  • Disadvantages: Naive Bayes is known to be a bad estimator.

In-depth explanation & Implementation

Multinomial Gaussian NB for spam detection


In the following video, the instructor used the spam detection example that involves some operations around text (NLP).

You don’t need to worry about those parts and instead focus on the parts revolving around Naive Bayes.

Naive Bayes Classification using Sklearn


You can download the slides for this topic from here.