Regression problems are about predicting a continuous value for an output variable given the values of one or more input variables. Instead, classification is about assigning a discrete value (representing a discrete class) to an output variable given some input variables. In both cases, the task is to get a model that properly models the mapping between output and input variables; in order to do so, we have at our disposal a sample with correct pairs of output-input variables. From a machine learning perspective, both regressions and classifications are instances of supervised learning algorithms.
My mother prepares a delicious dish called sopa seca, which is basically a spaghetti-based recipe and literally means dry soup. While it may sound like a misnomer or even an oxymoron, the name of the dish makes total sense when we learn how it is cooked. Something...
