Quality Vs. Quantity: The Impact Of Data Labeling Accuracy On Ai Performance

Data labeling is a crucial step in machine learning that involves tagging and categorizing raw data to help algorithms identify patterns and make predictions. The balance between data quality and quantity is important for developing robust AI models.