What is the main advantage of Naive Bayes in terms of computation?

Naive Bayes is known for its computational efficiency, which is the main advantage when dealing with machine learning tasks, especially with large datasets. The algorithm operates on a straightforward probabilistic model that assumes features are independent given the class label. This independence assumption simplifies the computation involved in classifying data points, as it allows the algorithm to calculate probabilities for each feature independently.

By leveraging Bayes' theorem, Naive Bayes can compute the likelihood of each class quickly, using the stored probabilities of features conditioned on class labels. As a result, even when the dataset size grows, the amount of computation needed does not increase significantly. This makes Naive Bayes particularly useful in applications like text classification and spam detection, where datasets can be quite large, yet rapid predictions are essential.

Other options presented do not align with the fundamental characteristics of Naive Bayes. For instance, it does not require extensive cross-validation compared to more complex models, nor does it consume a lot of memory resources. Additionally, it is not a complex algorithm; its simplicity contributes to its speed and efficiency in computation.