3-1-2. Loss function:
A loss function [13] is a mathematical function used to measure the
difference between the predicted output and the true output in machine
learning. The goal of training a model is to minimize the loss function,
indicating that the model’s predictions are close to the true output.
Loss functions can be different for different types of problems, such as
classification and regression, and must be carefully chosen to match the
requirements of the problem. The choice of loss function can greatly
impact the model’s performance, as different loss functions may
prioritize different aspects of the prediction, such as accuracy or
robustness.