Create a computer vision system using decision tree algorithms to solve a real-world problem : Bayes Theorem and Naive B

The video tutorial covers various classification methods in machine learning, focusing on Naive Bayes and support vector machines. It explains Bayes theorem and its application in medical and drug testing, highlighting the concept of false positives. The tutorial also demonstrates how Naive Bayes can be used for spam email classification, using scikit-learn for practical implementation.

Describe how Bayes theorem can be applied to build a spam classifier.

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What are the challenges of using Naive Bayes for spam classification?

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What assumptions does the Naive Bayes model make about the data?

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