Bernoulli Naive Bayes is a variant of the Naive Bayes classifier that assumes binary (0 or 1) features for the input data. It is particularly suited for text classification tasks, where the presence or absence of words in a document is important for determining the class label. This model relies on Bayes' theorem and assumes that features are conditionally independent given the class label, making it computationally efficient and effective in handling high-dimensional datasets.
congrats on reading the definition of Bernoulli Naive Bayes. now let's actually learn it.