Which learning approach combines labeled and unlabeled data, typically using the labeled data to predict pseudo-labels for the rest?

Which learning approach combines labeled and unlabeled data, typically using the labeled data to predict pseudo-labels for the rest?

• A. Supervised Learning
• B. Unsupervised Learning
• C. Semi-supervised Learning
• D. Lack of Accountability

Answer: (C)

Combining labeled and unlabeled data is a semi-supervised learning approach. It starts with a small set of labeled examples to train an initial model, then uses the model to predict labels for the abundant unlabeled data. Those predicted labels, called pseudo-labels, are treated as if they were true labels and added to the training data to refine the model. This leverages the structure and distribution of the unlabeled data to improve performance beyond what the limited labels alone can achieve, while still anchored by the real labeled examples. It sits between supervised learning (which uses only labeled data) and unsupervised learning (which uses no labels at all).

Combining labeled and unlabeled data is a semi-supervised learning approach. It starts with a small set of labeled examples to train an initial model, then uses the model to predict labels for the abundant unlabeled data. Those predicted labels, called pseudo-labels, are treated as if they were true labels and added to the training data to refine the model. This leverages the structure and distribution of the unlabeled data to improve performance beyond what the limited labels alone can achieve, while still anchored by the real labeled examples. It sits between supervised learning (which uses only labeled data) and unsupervised learning (which uses no labels at all).