What is supervised machine learning?
Supervised machine learning is a subset of machine learning that operates under a tightly defined set of rules. In this approach, algorithms learn from a preexisting labeled data set, also known as training data, where both the input and the desired output are provided. The goal is for the algorithm to learn a general rule that can automatically map well-known inputs to the desired outputs.
For example, imagine you are teaching a computer to recognize images of cats. You supply the algorithm with several pictures, each clearly labeled as "cat" or "not cat." The computer then tries various variables, methods, and analysis algorithms until it finds strict rules that produce the expected results. After completing this training phase, the algorithm should be able to correctly classify new, unlabeled images into the same categories.