👉 Accuracy computing refers to the process of evaluating and improving the precision, recall, and other metrics of machine learning models to ensure they deliver reliable and consistent results. It involves analyzing model outputs against ground truth data to identify discrepancies, such as false positives and false negatives, and using this feedback to refine the model's parameters, architecture, or training data. Techniques like cross-validation, error analysis, and calibration are employed to enhance model performance, ensuring that predictions are as close as possible to the true values. This iterative process of assessment and adjustment is crucial in fields like healthcare, finance, and autonomous systems, where high accuracy is paramount for decision-making and safety.
