Which classification technique is based on Bayes' theorem and assumes independence between predictors?

The classification technique that is based on Bayes' theorem and assumes independence between predictors is Gaussian Naive Bayes, which follows the Naive Bayes classification method. This method relies on the assumption that the features (or predictors) used in the classification are independent of each other given the class label, which is a key tenet of Naive Bayes classifiers.

Bayes' theorem provides a formula that allows the calculation of the posterior probability of a class label given input feature values. The "naive" assumption greatly simplifies the computation, making it efficient and effective for many types of classification tasks, especially when dealing with high-dimensional data.

The other options, such as Linear Support Vector Machine, Decision Tree, Logistic Regression, and XGBoost, do not operate based on Bayes' theorem, nor do they characterize themselves through the independence assumption of predictors in the same manner as Naive Bayes does. Each of these methods has its own theoretical foundation and assumptions regarding the relationships among predictors.