In which type of learning is Naive Bayes commonly used?

Naive Bayes is commonly used in supervised learning because it is a classification algorithm that uses the principles of Bayes' theorem, which applies when labeled training data is available. In supervised learning, the model is trained on a dataset that contains input-output pairs, allowing it to learn the relationship between the features (inputs) and the target labels (outputs).

Naive Bayes assumes that the features are independent given the class label, which simplifies the calculation of probabilities. This makes it particularly effective for tasks such as text classification, sentiment analysis, and spam detection, where the algorithm is provided with labeled examples to learn from.

In contrast, reinforcement learning focuses on learning through interaction with an environment and receiving feedback in the form of rewards or penalties, while unsupervised learning deals with unlabeled data to find hidden patterns or intrinsic structures. Deep learning involves neural networks with many layers and is generally associated with more complex models compared to Naive Bayes. Therefore, the application of Naive Bayes in supervised learning is well-founded in its design and functionality.