Weak Supervision
Weak supervision is a machine learning methodology that uses noisy, limited, or imprecise sources to generate training labels for supervised learning models, rather than relying on expensive and time-consuming manual annotation. It involves combining multiple weak labeling sources, such as heuristics, rules, or external knowledge bases, to create a probabilistic training dataset. This approach enables rapid development of machine learning models in domains where high-quality labeled data is scarce or costly to obtain.
Developers should learn weak supervision when building machine learning applications in data-rich but label-poor environments, such as natural language processing, computer vision, or healthcare, where manual annotation is impractical. It is particularly useful for prototyping, scaling models to new domains, or handling large unlabeled datasets efficiently. By leveraging weak supervision, teams can accelerate model development, reduce costs, and bootstrap learning from minimal supervision.