Loss Function

A loss function is a function that numerically expresses the discrepancy (error) between the output predicted by a machine learning model and the actual ground-truth label. It provides a quantitative measure of how "wrong" the model is, and serves as the benchmark for evaluating model performance. During training, the parameters (weights and biases) of the model are adjusted with the goal of minimizing the value of the loss function. A large loss function value means the prediction is far from the truth; a small value means the model is accurate. In neural networks, backpropagation is used to compute gradients based on this loss, and the parameters are then optimized accordingly. The main types of loss functions are used according to the task type: [Loss functions for regression] • Mean Squared Error (MSE): The average of squared errors. Sensitive to outliers. • Mean Absolute Error (MAE): The average of absolute errors. Somewhat more robust to outliers. [Loss functions for classification] • Cross-Entropy Loss: Loss applied to probabilistic outputs. The standard for classification tasks. • Hinge Loss: Used with Support Vector Machines (SVM). [Other loss functions] • KL Divergence (Kullback-Leibler Divergence) • Huber loss, and many others tailored to specific tasks and models. Choosing the right loss function is critically important, as it has a major impact on model accuracy and training stability. In practice, custom loss functions engineered for specific tasks and composite designs that combine multiple metrics are also widely used. The loss function is the foundational mechanism by which AI learns "what it got wrong," and is one of the indispensable elements of machine learning and deep learning.