Naive Bayes is a type of statistical classifier used in machine learning that is based on the Bayes Theorem. It is a supervised learning algorithm that is used to classify data based on input data and prior probabilities. Naive Bayes is one of the most popular and widely used algorithms due to its simplicity, accuracy, and low computational cost.
Compared to other classifiers, Naive Bayes has some distinct advantages.
It is fast, simple, and easy to implement, and does not require a lot of data to produce good results. It is also able to handle a large number of features, and is able to make predictions about unseen data. Additionally, since it is based on probabilities, it is able to handle uncertain information better than other classifiers.
However, Naive Bayes also has some drawbacks compared to other classifiers. Since it assumes that all features are independent of each other, it may not be able to accurately predict relationships between variables. Additionally, it is not able to capture complex relationships between variables, and may not be able to capture non-linear relationships.
See for example this picture that compares NB with decision trees: 
Example:
Let's say we have a dataset of customer purchase data and we want to classify customers into two groups: those who will buy a product and those who won't. We could use Naive Bayes to classify the data. We would first need to create a training set using the customer purchase data. We would then use the training set to create a model that can predict whether a customer is likely to buy a product or not. Once we have the model, we can then use it to predict whether a new customer is likely to buy a product or not.