Answer Posted / Priti Paliwal

Naive Bayes is called naive because of its assumption of independence, which is often referred to as the independence assumption. It assumes that all the features (or variables) are conditionally independent given the class variable. This simplification makes the algorithm computationally efficient but may not always hold true in real-world scenarios. Despite this limitation, Naive Bayes has been found to perform well in many classification tasks.

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