Which of the following is a common application of Naive Bayes?

Naive Bayes is particularly well-suited for classification tasks where the goal is to predict the category of a given data point based on its features. One of the most common applications of Naive Bayes is in spam detection. This algorithm works by using Bayes' theorem to calculate the probabilities of a message being spam or not, based on the presence of certain words or phrases within the email content.

In spam detection, the classifier is trained on a dataset of labeled emails, with features extracted from the text such as the frequency of specific words. The Naive Bayes model assumes that the presence of a particular feature (like a word related to marketing) in an email is independent of the presence of other features, which simplifies computations and allows for rapid classification of new emails as either spam or not spam.

Other applications mentioned, such as self-driving cars, facial recognition, and game strategy optimization, typically involve more complex models and algorithms that take into account numerous interdependencies and require advanced techniques, including deep learning and reinforcement learning. Naive Bayes, while effective for certain problems, is primarily utilized in scenarios where the simplicity and efficiency of the model are advantageous, making it the appropriate choice for spam detection.