Loss Model

A loss model calculates the difference between a model's predictions and the desired output, typically expressed as a numerical value. It serves as a training signal, helping algorithms reduce errors over time through gradient-based updates. Loss models are foundational to supervised and reinforcement learning workflows, ensuring that models optimize toward specific goals.