What is the purpose of post-processing in machine learning?

The purpose of post-processing in machine learning is to adjust the outputs of a model to improve fairness or meet specific business needs. After a model has been trained and produces predictions, post-processing techniques can be employed to ensure that these predictions align better with desired ethical standards or practical requirements. This process might involve recalibrating scores, mitigating biases in the outputs, or applying further criteria to make the results more applicable in real-world scenarios.

By focusing on refining the predictions after the model has been trained, organizations can address potential issues that may arise from the model's inherent biases or limitations, thereby enhancing the utility and fairness of the model's outcomes. This step is crucial in deploying machine learning models responsibly and ethically, ensuring that the results are not only accurate but also equitable and aligned with overarching objectives.