Binary and Beyond Binary Classification
Kolla Bhanu Prakash · 2024
Binary and beyond binary classifications are mainly classified as follows: classification, scoring and ranking, class probability estimation, handling more than two classes, and descriptive learning. Descriptive learning theories make statements about how learning occurs and devise models that can be used to explain and predict learning results. Binary classification problems often require two classes—one representing the normal state and the other representing the aberrant state. One of the most often used machine learning algorithms, within the category of supervised learning, is logistic regression. The output of a classification algorithm is shown and summarized in a confusion matrix. The chapter covers the topics of how to assess multi-class performance and how to construct multi-class models from binary models. The difference between multi-class classification and multi-label classification is that the tasks in multi-label problems are all related in some way.