What classification method is based on applying Bayes' theorem with strong independence assumptions?

The classification method that relies on applying Bayes' theorem with strong independence assumptions is Naive Bayes. This algorithm operates under the premise that the features used for prediction are independent of one another given the class label. This simplification allows Naive Bayes to efficiently compute the posterior probabilities of classes based on the training data.

The strength of Naive Bayes lies in its ability to handle high-dimensional data well and perform well with relatively small sample sizes, especially in text classification tasks such as spam detection. Despite the independence assumption being a simplification that may not hold in practice, Naive Bayes often produces surprisingly effective results.

In contrast, other methods like logistic regression, K-nearest neighbors, and support vector machines do not base their predictions on this specific application of Bayes' theorem and independence assumptions, which differentiates Naive Bayes as the fitting answer to this question.