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.