A data scientist is tasked with building a predictive model to forecast sales based on historical data. To ensure the model performs well, they decide to apply empirical risk minimization (ERM). Which of the following actions best represents the application of ERM in this scenario?

Empirical risk minimization means picking the model that makes the smallest average error on the data you already have. The data scientist would compute a loss (such as mean‑squared error) for every candidate model on the historical sales data and then choose the one that gives the lowest average loss. For example, if two regression models give average errors of 5 % and 3 % on the training set, ERM would pick the one with 3 %. This approach focuses on the training data’s performance as a proxy for how well the model will predict new sales.