R Tutorial: Classification, Regression, Clustering

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-machine-learning-with-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Well done! If you got this far, you already have an idea about some machine Learning problems. Let's get a bit more specific and discuss three common types of machine learning problems: Classification, Regression and Clustering. These are very broad topics, so I'll stick to a brief introduction in this video to get you familiar with these techniques. The three last chapters of this course go into more detail. First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class. The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself? What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sic