R Tutorial: Data preparation for kNN

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. --- You've now seen the kNN algorithm in action while simulating aspects of a self-driving vehicle. You've gained an understanding of the impact of k on the algorithm's performance, and know how to examine the neighbors' votes to better understand which predictions are closer to unanimous. But before applying kNN to your own projects, you'll need to know one more thing: how to prepare your data for nearest neighbors. As noted previously, nearest neighbor learners use distance functions to identify the most similar, or nearest examples. Many common distance functions assume that your data are in numeric format, as it is difficult to define the distance between categories. For example, there's no obvious way to define the distance between "red" and "yellow"; consequently, the traffic sign dataset represented these using numeric color intensities. But suppose that you have a property that cannot be measured numerically, such as whether a road sign is a rectangle, diamond, or octagon. A common solution uses 1/0 indicators to represent these categories. This is called dummy coding. A binary "dummy" variable is created for each category except one. This variable is set to '1' if the category applies and '0' otherwise. The category that is left out can be easily deduced, if the stop sign is not a rectangle or a diamond, then it must be an octagon. Dummy coded data can be used directly in a distance function; two rectangle signs, both having values of '1', will be found to be closer together than a rectangle and a diamond. It is also important to be aware that when calculating distance, each feature of the input data should be measured with the same range of values. This was true for the traffic sign data; each color component ranged from