What does "post processing" involve in the context of machine learning?

Post-processing in the context of machine learning refers to the adjustments or modifications made to a model's output after the model has completed its prediction task. This stage is crucial as it can enhance the interpretability, accuracy, and usability of the results generated by the model.

For example, post-processing may involve applying thresholds to classify probabilities into discrete categories, refining the outputs based on domain-specific rules, or even adjusting the final predictions based on additional information or constraints that were not incorporated during the model training phase. This can improve the overall performance and ensure that the output aligns better with practical requirements.

In contrast, the other options relate to different phases of the machine learning lifecycle. Cleaning data before training involves preparing raw data to ensure its quality for model input, while training a model on a cleaned dataset aligns with the model training phase itself. Lastly, model selection based on performance metrics is an evaluative step that occurs after training and before deployment, as it involves choosing the best model based on validation results. Thus, these processes are distinct from post-processing, which pertains specifically to what happens after the model generates its outputs.