Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.

In the context of an Artificial Intelligence interview question, a concise and accurate answer to “What is Naive Bayes?” would be:

“Naive Bayes is a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. It is widely used in machine learning for text classification, spam filtering, and other applications where the dataset features are assumed to be conditionally independent given the class label.”

This response covers the fundamental concept of Naive Bayes, its reliance on Bayes’ theorem, its application in machine learning tasks, and the underlying assumption of feature independence.