What probability does Naive Bayes calculate to make predictions?

Naive Bayes is a classification algorithm based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Specifically, it calculates the conditional probability of each feature given a particular class label. This means that for each feature, it assesses how likely that feature is to occur when a certain class is present.

In practical terms, when making predictions, Naive Bayes evaluates the likelihood of various classes based on the observed features, using the conditional probabilities calculated from the training data. The algorithm then selects the class with the highest conditional probability, which is the probability of each feature being present if a certain class is the outcome. This approach is integral to how Naive Bayes performs classification and illustrates its efficiency in predicting outcomes based on learned data distributions.

The other probabilities mentioned do not align with the foundational approach of Naive Bayes. For instance, calculating the joint probability of all features would not be feasible due to the independence assumption, marginal probability focuses on individual feature distributions without the context of class labels, and Bayesian probability of the highest outcome could misinterpret the algorithm’s goals. Overall, the algorithm’s design revolves around understanding the conditional relationships between features and classes, making the choice of conditional probability correct.