Naive Bayes is so ‘naive’ because it assumes that all of the features in a data set are equally important and independent. As we know, these assumptions are rarely true in real-world scenarios.
Naïve Bayes machine learning algorithm is considered Naïve because the assumptions the algorithm makes are virtually impossible to find in real-life data. Conditional probability is calculated as a pure product of individual probabilities of components. This means that the algorithm assumes the presence or absence of a specific feature of a class is not related to the presence or absence of any other feature (absolute independence of features), given the class variable. For instance, a fruit may be considered to be a banana if it is yellow, long and about 5 inches in length.
However, if these features depend on each other or are based on the existence of other features, a naïve Bayes classifier will assume all these properties to contribute independently to the probability that this fruit is a banana. Assuming that all features in a given dataset are equally important and independent rarely exists in the real-world scenario.