Answer Posted / Atul Kumar Singh

Naive Bayes is called Naive because it assumes that the presence or absence of a particular feature in a class is independent of the presence or absence of any other feature. This assumption may not always hold true, but even with this simplification, Naive Bayes can still perform well for many classification problems due to its computational efficiency and ability to handle large amounts of data.

What are standardization and normalisation? Give one advantage of each over the other?

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Do you have research experience in machine learning?

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Tell me what are the last machine learning papers you've read?

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