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Explain how does naive bayes classifier work in machine learning?
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Answer / Harshit Kumar
The Naive Bayes Classifier in machine learning is a probabilistic classifier based on applying Bayes' Theorem with strong independence assumptions between features. It assumes that the presence of a particular feature in a class is unrelated to the presence or absence of any other feature. In simpler terms, given a set of input features, it predicts which class or category they belong to by calculating the probability of each class given the observed features.
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