Understanding Supervised Learning In One Article

As you might know, supervised machine learning is one of the most commonly used and successful types of machine learning. In this article, we will describe supervised learning in more detail and explain several popular supervised learning algorithms. Remember that supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input/output pairs. We build a machine learning model from these input/output pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data. Supervised learning often requires human effort to build the training set, but afterwards automates and often speeds up an otherwise laborious or infeasible task. There are two major types of supervised machine learning problems, called classification and regression. In classification, the goal is to predict a class label, which is a choice from a predefined list of possibilities.