What is the primary objective of classification in machine learning?

The primary objective of classification in machine learning is to categorize data points into predefined classes. Classification is a type of supervised learning where the model learns from labeled training data, allowing it to make predictions about the category to which new, unseen examples belong. During training, the algorithm analyzes the features of the labeled data to identify patterns that differentiate between the various categories or classes.

In this context, the focus is specifically on assigning input data into distinct classes rather than predicting future values or discovering underlying patterns within unlabeled data. While identifying patterns may be a goal in other contexts, such as clustering algorithms, classification is specifically about working with labeled data to achieve accurate categorizations. Therefore, the emphasis on predefined classes is what makes this option the correct choice.