Multiclass Learning

Multiclass learning is a type of machine learning where a system is trained to categorize data into more than two possible classes or labels. Unlike binary classification, which sorts data into two groups, multiclass learning deals with problems like recognizing handwritten digits (0-9) or identifying different types of animals. The goal is for the model to accurately assign each input to the correct class based on features it has learned from training data. This approach is common in applications requiring multiple categories, helping systems make more nuanced and precise decisions.