Which learning approach uses both labeled and unlabeled data to improve model performance?

Which learning approach uses both labeled and unlabeled data to improve model performance?

• A. Semi-supervised learning
• B. Supervised learning
• C. Unsupervised learning
• D. Reinforcement learning

Answer: (A)

Semi-supervised learning blends labeled and unlabeled data to improve model performance. It sits between supervised and unsupervised learning: you have a small set of labeled examples that provide the correct targets, plus a much larger set of unlabeled examples that reveal the structure of the input space. The unlabeled data helps the model learn where data points lie, groupings, and relationships, which can guide the labeling decisions and improve generalization when labeled data is scarce or expensive to obtain. Techniques include self-training, where the model assigns pseudo-labels to unlabeled data and retrains, and graph-based or consistency-regularization methods that leverage the relationships among all data points to propagate information. In contrast, supervised learning relies only on labeled data, unsupervised learning uses only unlabeled data to discover patterns, and reinforcement learning learns from interactions with an environment via rewards. Using both labeled and unlabeled data to boost performance is the defining feature of semi-supervised learning.

Semi-supervised learning blends labeled and unlabeled data to improve model performance. It sits between supervised and unsupervised learning: you have a small set of labeled examples that provide the correct targets, plus a much larger set of unlabeled examples that reveal the structure of the input space. The unlabeled data helps the model learn where data points lie, groupings, and relationships, which can guide the labeling decisions and improve generalization when labeled data is scarce or expensive to obtain. Techniques include self-training, where the model assigns pseudo-labels to unlabeled data and retrains, and graph-based or consistency-regularization methods that leverage the relationships among all data points to propagate information.

In contrast, supervised learning relies only on labeled data, unsupervised learning uses only unlabeled data to discover patterns, and reinforcement learning learns from interactions with an environment via rewards. Using both labeled and unlabeled data to boost performance is the defining feature of semi-supervised learning.