loss function

This term is a technical cornerstone of supervised learning and optimization. It quantifies the cost of inaccuracy, where a higher value indicates a poorer model performance. The primary goal of training a model is to minimize this value through algorithms like gradient descent.

Different loss functions are selected based on the nature of the task. For instance, Mean Squared Error is standard for regression problems to penalize large errors, while Cross-Entropy loss is the industry standard for classification tasks to measure the distance between probability distributions.