The naive Bayes classifier is a simple yet powerful classification algorithm based on Bayes' theorem and conditional probability. It assumes that the presence of a particular feature in a class is independent of the presence of other features. This conditional independence assumption makes the algorithm efficient and allows it to work well even with a small amount of training data.

The classifier calculates the probability of a data point belonging to a particular class by multiplying the individual probabilities of each feature given that class. The class with the highest probability is assigned to the data point.

For example, in text classification, the naive Bayes classifier can be used to determine whether a given document belongs to a specific category, such as spam or not spam. It does this by considering the frequency of words in the document and the conditional probabilities of those words given the class of the document.

Implementing a naive Bayes classifier involves estimating the prior probabilities of each class and the likelihood probabilities of each feature given each class. Evaluation of the classifier can be done using metrics like accuracy, precision, and recall.