The Naive Bayes algorithm is a simple yet powerful classification algorithm based on Bayes' theorem. It assumes that the features are conditionally independent given the target variable. This assumption allows Naive Bayes to efficiently compute the probability of a class given the feature values.

To illustrate this, let's consider a classification problem of predicting whether an email is spam or not. We can represent an email as a set of text features like the presence of certain keywords or the frequency of specific words. By applying the Naive Bayes algorithm to this problem, we can estimate the probability of an email being spam or not based on the occurrence of these features.

On the other hand, Bayesian networks provide a graphical representation of probabilistic relationships between variables. They can enhance classification tasks by capturing dependencies among features and the class variable. For instance, in the email spam detection problem, a Bayesian network can model the relationships between the appearance of particular words and the probability of an email being classified as spam.

By leveraging Naive Bayes and Bayesian networks, we can effectively handle classification problems with high-dimensional feature spaces and limited training data.