Classification is a fundamental concept in machine learning, particularly in supervised learning. It involves categorizing data into distinct groups based on specific features. The primary goal is to predict the class label of new instances based on the learned patterns from the training data.

There are several types of classification tasks. The most basic is binary classification, where data instances are assigned to one of two classes, typically represented as 0 and 1. An example of this could be determining whether a medical test result indicates the presence or absence of a disease. In contrast, multiclass classification involves more than two classes, such as categorizing images of animals into dogs, cats, or rabbits. Additionally, multilabel classification allows a single instance to belong to multiple classes simultaneously, where each class is binary in nature ²⁵.

Classification has a wide range of applications, including spam detection, image recognition, and medical diagnosis, making it a vital area of study in data science and artificial intelligence ¹².