What step involves training a machine learning model to classify text into predefined categories based on extracted features?

The correct choice involves the step where a machine learning model is trained to automatically assign text to specific categories, based on features that have been extracted from the text. This process is known as text classification.

In text classification, algorithms are utilized to learn from a set of training data, where the text samples are already categorized. The model examines various characteristics of the text, such as keywords, phrases, and patterns, to establish the relationship between the features and the categories. Once trained, the model can then categorize new, unseen text into the predefined classes, making it a vital process in natural language processing (NLP) tasks, including those in medical billing and coding.

Regarding the other options, discourse analysis focuses on the structure of spoken or written communication and how language is used in context, rather than classifying text into categories. Sentiment analysis is specifically aimed at determining the sentiment or emotion expressed in a piece of text, classifying it as positive, negative, or neutral, rather than assigning it to broader categories. Entity recognition, on the other hand, identifies and classifies entities within the text, such as names of medications, patients, or procedures, instead of classifying the entire text into categories.