Which approach is a semi-supervised method that predicts labels only for the unlabeled data at hand?

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Multiple Choice

Which approach is a semi-supervised method that predicts labels only for the unlabeled data at hand?

Transductive semi-supervised learning is about labeling only the unlabeled examples that come with the current dataset. It uses both the labeled data and the structure of the unlabeled data to assign those labels, but it doesn’t aim to build a model that works on new, unseen instances. That fits the requirement of predicting labels specifically for the unlabeled data at hand. Inductive semi-supervised learning, in contrast, seeks a generalizable model that can label future data, while unsupervised learning has no labels to begin with. So the described approach is transductive semi-supervised learning.

Transductive semi-supervised learning is about labeling only the unlabeled examples that come with the current dataset. It uses both the labeled data and the structure of the unlabeled data to assign those labels, but it doesn’t aim to build a model that works on new, unseen instances. That fits the requirement of predicting labels specifically for the unlabeled data at hand. Inductive semi-supervised learning, in contrast, seeks a generalizable model that can label future data, while unsupervised learning has no labels to begin with. So the described approach is transductive semi-supervised learning.

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