Classification algorithms

Classification is an important and popular machine learning tool that assigns items in a data set to different categories. Classification is used to predict risk over time, in fraud detection, text categorization, and more. Classification functions begin with a data set where the different categories are known. For example, suppose you want to classify students based on how likely they are to get into graduate school. In addition to factors like admission score exams and grades, you could also track work experience.

Binary classification means the outcome, in this case, admission, only has two possible values: admit or do not admit. Multiclass outcomes have more than two values. For example, low, medium, or high chance of admission. During the training process, classification algorithms find the relationship between the outcome and the features. This relationship is summarized in the model, which can then be applied to different data sets, where the categories are unknown.