Which of the following best describes the role of hyperparameter tuning in optimizing multi-class loss functions in a business context?

Hyperparameter tuning is like fine‑tuning a recipe so the machine learning model learns the best way to separate the different classes. In a business setting it means adjusting settings such as learning rate, regularization strength, or tree depth to reduce the multi‑class loss, which measures how far the predictions are from the true categories. By lowering this loss, the model gives more accurate predictions for tasks such as product recommendation or fraud detection, which can save money or improve customer satisfaction. For example, a retailer might tune the depth of a decision tree to better distinguish high‑value from low‑value customers, cutting misclassification costs. Thus, hyperparameter tuning directly improves the model’s performance and the business’s bottom line.