Description: The term ‘multi-class’ in the context of machine learning refers to classification problems that involve multiple classes or categories. Unlike binary classification, where there are only two possible outcomes, multi-class classification seeks to assign an instance to one of several possible classes. This approach is fundamental in various applications, as many real-world problems are not limited to two categories. The main characteristics of multi-class problems include the need for algorithms that can handle multiple outputs and the additional complexity in evaluating model performance. Machine learning models, such as decision trees, support vector machines, and neural networks, can be adapted to tackle multi-class problems using techniques like ‘one-vs-all’ or ‘one-vs-one’ encoding. The relevance of multi-class classification lies in its ability to solve complex problems in areas such as computer vision, natural language processing, and bioinformatics, where decisions are not simply binary but require a more nuanced and detailed classification.
Uses: Multi-class classification is used in a variety of applications, such as image recognition, where a model can identify different objects in an image, like cats, dogs, and cars. It is also applied in natural language processing to classify texts into different categories, such as news, sports, or entertainment. In the medical field, it is used to diagnose diseases based on symptoms that may correspond to multiple conditions. Additionally, in recommendation systems, it can be employed to classify products into different categories based on user preferences.
Examples: An example of multi-class classification is the handwritten digit recognition model MNIST, which classifies images of digits from 0 to 9. Another case is the email classification system that categorizes messages into ‘spam’, ‘promotions’, and ‘primary’. In the healthcare field, a model can classify different types of diseases based on clinical and laboratory data.
