What is the classification algorithm that utilizes Bayes' theorem to compute classification probabilities called?

The classification algorithm that uses Bayes' theorem to compute classification probabilities is known as Naïve Bayes. This algorithm is based on the principle of applying Bayes' theorem with the assumption that the features are conditionally independent given the class label. This means that Naïve Bayes calculates the probability of a particular class based on the features present in the dataset, treating each feature as an independent contributor to the outcome.

The strength of Naïve Bayes lies in its simplicity and efficiency, making it particularly well-suited for large datasets and problems where the features are indeed conditionally independent or near-independent. It is widely used for text classification tasks, such as spam detection and sentiment analysis, because it performs exceptionally well even with large vocabularies.

Understanding the probabilistic foundation of Naïve Bayes and its underlying assumptions helps clarify why it can be effective in various machine learning applications, especially when compared to other algorithms that may not utilize probabilistic reasoning in the same way.