Definition
A loss function, also known as a cost function, is a mathematical expression that quantifies the error between the predicted output of a machine learning model and the actual target value. It essentially measures how poorly the model is performing on a given task. During the training process, the goal is to minimize this loss function by adjusting the model's internal parameters. This minimization drives the model to learn patterns and make more accurate predictions over time. For instance, in image classification, a loss function would penalize the model heavily if it incorrectly identifies a cat as a dog. It's crucial to distinguish loss functions from evaluation metrics, as loss functions are used for optimization during training, while evaluation metrics assess the final model performance. Understanding loss functions is fundamental for many machine learning and data science certifications, including those covering AI and machine learning domains.