What is the function of a loss function in a model?

The loss function plays a critical role in a model by quantifying how well the model's predictions align with the actual outcomes of the data. In the context of machine learning and artificial intelligence, the primary purpose of the loss function is to provide a scalar value that represents the difference between the predicted values generated by the model and the actual values from the dataset. This quantification allows for the evaluation of the model’s performance during training and inferencing.

The loss function guides the optimization process used to adjust the model's parameters. By calculating the loss after each prediction, the algorithm can determine whether to increase or decrease the parameter values to minimize the loss in future predictions. Thus, the choice of loss function can significantly affect the model's learning efficiency and effectiveness, helping to drive improvements in model accuracy over time.

In summary, the function of the loss function is crucial in training models, serving as a benchmark to measure how closely the model's predictions relate to real-world data, ultimately leading to better model performance through continual adjustments.