Since 2020, aggregated from related topics
Semi-supervised learning is a machine learning technique that falls between supervised learning (where data is fully labeled) and unsupervised learning (where data is unlabeled). In semi-supervised learning, a small portion of the data is labeled, while the majority of the data remains unlabeled. The goal of semi-supervised learning is to leverage both the labeled and unlabeled data to improve the model's performance and accuracy. This approach is often used when labeled data is expensive or time-consuming to obtain, but unlabeled data is abundant. The challenge in semi-supervised learning is to effectively incorporate the unlabeled data to enhance the model without introducing noise or bias.