👉 Scoring engineering is the process of designing and optimizing a model's loss function to guide its learning process effectively. It involves crafting a mathematical expression that quantifies the discrepancy between predicted outputs and actual targets, enabling the model to adjust its parameters to minimize this error. This is achieved by assigning weights to different components of the loss, allowing the model to prioritize certain aspects of prediction over others. For instance, in regression tasks, the loss might be a weighted mean squared error, where weights reflect the importance of different predictions. In classification tasks, it could be a weighted cross-entropy loss, emphasizing the correctness of predictions for more critical classes. By carefully engineering the scoring function, developers can influence the model's behavior, steering it towards desired outcomes and improving overall performance.
