Which of the following best describes the training process of a Naive Bayes classifier?

The training process of a Naive Bayes classifier is characterized by its focus on estimating probabilities from the training data. This method is grounded in Bayes' theorem, which relates the conditional and marginal probabilities of random variables. In practice, the classifier assumes that the features are independent given the class label, allowing it to compute the probability of each class based on the observed features efficiently.

During training, the Naive Bayes algorithm calculates the prior probabilities of each class and the conditional probabilities of the features given each class. This probabilistic foundation enables the model to predict the class of new instances by applying Bayes' theorem: it combines the prior probability of the class with the likelihood of the features to determine the posterior probabilities of each class.

In contrast, the other options do not accurately represent the process involved in training a Naive Bayes classifier. Deep learning techniques, such as neural networks, are much more complex and do not align with the simplistic probabilistic approach of Naive Bayes. Fitting a decision tree model refers to a different algorithm entirely that uses a tree structure for classification or regression, which is not what Naive Bayes does. Lastly, reinforcement feedback pertains to reinforcement learning, a distinct area where agents learn through interactions with their environment