👉 Commissions math, often used in the context of machine learning and neural networks, is a method where a model's performance is evaluated based on the financial incentive it generates. Instead of relying solely on traditional metrics like accuracy or loss, commissions math focuses on the total revenue or profit a model brings to its owner. This is achieved by defining a reward function that quantifies the value of each prediction the model makes, typically in terms of user engagement or business outcomes. The goal is to optimize the model's predictions so that they maximize this reward, aligning the model's objectives with the business's financial goals. This approach is particularly useful in applications like recommendation systems, where the ultimate measure of success is the revenue generated from user interactions.
