Description: Multiclass classification is a machine learning task where the goal is to predict the class of instances belonging to multiple categories. Unlike binary classification, which has only two possible classes, multiclass classification involves three or more classes. This type of classification is fundamental in various artificial intelligence applications, where a model is required to distinguish between multiple categories. The main characteristics of multiclass classification include the need for algorithms that can handle multiple outputs and the ability to evaluate model performance using specific metrics such as accuracy, recall, and F1 score. In the context of machine learning, various algorithms, including decision trees, support vector machines, and neural networks, can be leveraged for multiclass classification tasks, as they can learn complex representations of data. Additionally, the use of AutoML techniques has made it easier to implement multiclass classification models, allowing users without programming experience to build machine learning models more accessibly. In the field of data mining and machine learning with big data, multiclass classification is applied in areas such as anomaly detection, where unusual patterns in large volumes of data are sought.
