What is the purpose of a loss function in machine learning?

The purpose of a loss function in machine learning is to quantitatively evaluate how well the model's predictions correspond to the actual outcomes. It provides a way to assess the accuracy of the model's predictions during the training process. By calculating the difference between predicted values and true values (targets), the loss function outputs a single numerical score that reflects the model's performance. This score is essential as it guides the optimization process; the goal of training a model is to minimize this loss score, leading to improvements in the model's predictive abilities. When the loss function indicates that the model's predictions are closely aligned with the true outcomes, it suggests that the model is performing well. Thus, understanding and utilizing the loss function is critical for effective machine learning model training and development.