
Iterative Dataset Refinement: Constantly Improving Quality Over Time
In machine learning, data rarely remains unchanged. Data is constantly reviewed, errors are corrected, and refinements are added to make the models more accurate. For example, if the algorithm often confuses objects in images, experts review the markup and make adjustments. This approach allows us to gradually improve data quality