What probabilistic classifier is based on Bayes' theorem with the assumption of feature independence?

The mention of a probabilistic classifier that is based on Bayes' theorem with the assumption of feature independence points directly to Naive Bayes. This classifier operates on the principle of Bayes' theorem, which describes the probability of a class given a certain set of features.

The term "naive" refers to the assumption that all features are independent of each other given the class label. This independence assumption simplifies the computation involved in determining the probabilities, making Naive Bayes computationally efficient and particularly effective for large datasets. Each feature contributes independently to the class probability, which, despite being a strong assumption that may not hold true in all real-world scenarios, often leads to surprisingly good performance, especially for text classification tasks.

In contrast, logistic regression, support vector machines, and decision trees do not rely on such assumptions about feature independence. Logistic regression models the relationship between features and the probability of a binary outcome without assuming independence among the predictors. Support vector machines focus on finding the optimal hyperplane to separate classes and do not inherently deal with probabilistic outputs. Decision trees create a model based on decision rules derived from the feature space without making independence assumptions between variables.

The unique characteristics of Naive Bayes make it particularly suitable for certain applications