👉 Score engineering is a technique used to enhance and optimize the performance of machine learning models by manipulating the scores or predictions generated by these models. It involves adjusting the output of a model, typically its probability scores, to better align with the desired output or to improve specific metrics like precision, recall, or F1 score. This can be achieved through various methods such as threshold tuning, where the decision threshold for classifying a positive instance is adjusted; calibration, which ensures that predicted probabilities reflect true likelihoods; or by incorporating additional loss functions that penalize certain types of errors more heavily. The goal is to produce scores that are more informative and useful for downstream tasks, leading to better model performance and decision-making.
