What is the main function of multiclass classification in machine learning?

The primary function of multiclass classification in machine learning is to label target data into multiple categories. In many real-world scenarios, the target variable can take on more than two values, and multiclass classification is specifically designed to handle such cases.

Unlike binary classification, which deals with two classes, multiclass classification allows an algorithm to predict which category a given input belongs to among a set of multiple categories. For example, if you were classifying types of fruits, your classes might be apples, oranges, and bananas. The model learns from the training data, distinguishing the various characteristics of each of the classes, and then accurately classifies new instances based on that learned information.

In this context, the other options represent different machine learning tasks that do not directly relate to multiclass classification. While anomaly detection focuses on identifying rare or unexpected observations in data, visualizing high-dimensional data is more about understanding complex relationships rather than categorization. Similarly, interpolating missing values pertains to estimating gaps in data rather than classifying data into distinct groups.