Data labeling and relabeling in machine learning

Data labeling is a crucial part of supervised machine learning, providing models with the necessary information to accurately classify data samples. This process involves assigning categories to data samples, as seen in examples like the ImageNet dataset, and is essential for building discriminative models. Labeled data enables models to predict labels for new, unseen data, but mislabeled data can introduce bias and reduce accuracy, necessitating relabeling efforts to correct errors and improve data quality. Data labeling is typically performed by annotators who use guidelines and tools to efficiently label data, although it can be time-consuming and prone to errors. To address these challenges, best practices include creating comprehensive annotation frameworks, leveraging crowdsourcing for initial labeling, and conducting error analysis to identify and correct mislabeled samples. Advanced techniques, such as weak supervision and active learning, are also employed to enhance labeling efficiency, ensuring high-quality data for training robust machine learning models.