Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 73

Here, we state the problem of supervised learning explicitly. We have a set of training data , where for all , and a corresponding set of labels , which can represent either a category membership or a real‐valued response. We aim to construct a function that maps each input to a predicted label . A given supervised learning method chooses a particular form , where is a vector of parameters based on .

We wish to choose to minimize an error function . The error function is most commonly taken to be the sum of square errors in which case the goal is to choose an optimal such that

where can be any loss function that evaluates the distance between and , such as cross‐entropy loss and square loss.