R Tutorial: Classification with Nearest Neighbors

Want to learn more? Take the full course at https://learn.datacamp.com/courses/supervised-learning-in-r-classification at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Brett Lantz and I'm a data scientist at the University of Michigan and the author of the book "Machine Learning with R." Machine learning utilizes computers to turn data into insight and action. This course focuses on a subset of machine learning. The sub-domain called supervised learning focuses on training a machine to learn from prior examples. When the concept to be learned is a set of categories, the task is called classification. From identifying diseases, predicting the weather, or detecting whether an image contains a cat, classification tasks are diverse yet common. In this course, you'll learn classification methods while exploring four real-world applications. Let's get started! If your experiences on the road are anything like mine, self-driving cars can't get here soon enough! It's easy to imagine aspects of autonomous driving that involve classification; for example, when a vehicle's camera observes an object, it must classify the object before it can react. Though the algorithms that govern autonomous cars are sophisticated, we can simulate aspects of their behavior. In this example, we'll suppose the vehicle can see but not distinguish the roadway signs. Your job will be to use machine learning to classify the sign's type. To start training a self-driving car, you might supervise it by demonstrating the desired behavior as it observes each type of sign. You stop at intersections, yield to pedestrians, and change speed as needed. After some time under your instruction, the vehicle has built a database that records the sign as well as the target behavior. The image here illustrates this dataset. I suspect you already see some similarities, the machine can too! A nearest neighbor classifier takes ad