Is Naïve Bayes bad? If yes, under what aspects.
Naïve Bayes is a machine learning algorithm based on the Bayes Theorem. This is used for solving classification problems. It is based on two assumptions, first, each feature/attribute present in the dataset is independent of another, and second, each feature carries equal importance. But this assumption of Naïve Bayes turns out to be disadvantageous. As it assumes that the features are independent of each other, but in real-life scenarios, this assumption cannot be true as there is always some dependence present in the given set of features. Another disadvantage of this algorithm is the ‘zero-frequency problem’ where the model assigns value zero for those features in the test dataset that were not present in the training dataset.
