👉 Afterwards computing, also known as post-processing or post-analysis, is the phase where the results and outputs generated by a machine learning model are refined, interpreted, and validated. This stage involves analyzing the model's predictions to ensure they are accurate, reliable, and aligned with human expectations or domain-specific standards. Afterwards computing includes tasks such as error analysis to identify and correct misclassifications, generating explanations for model decisions to enhance transparency, and applying data preprocessing or feature engineering to improve the quality of input data for future iterations. It also involves creating visualizations and reports to communicate findings effectively to stakeholders and facilitating iterative model improvement by incorporating feedback from this stage.
