A loss function is a crucial component in machine learning and deep learning, serving as a metric to evaluate how well a model’s predictions align with the actual outcomes. It quantifies the difference between the predicted values and the true values, guiding the optimization process during training. By minimizing the loss function, the model adjusts its parameters to improve accuracy. Loss functions can be categorized into two main types: those used for classification tasks and those for regression tasks. Selecting the appropriate loss function is essential, as it directly influences the model’s performance and learning efficiency.