In supervised learning, the goal is to learn a function that is able to map inputs x to outputs y given a labeled set of input-output pairs D, where D is referred to as the training set and N is the number of input-output pairs in the training set:
In simple applications of supervised learning models, each training input xi is a numerical vector representing model features such as price, age, and temperature. In complex applications, xi may represent more complex objects, such as a time series, images, and text.
When the output yi (also called the response variable) is categorical in nature, then the problem is referred ...
