Semi-supervised learning is a type of machine learning.
It refers to a learning problem (and algorithms designed for the learning problem) that involves a small portion of labeled examples and a large number of unlabeled examples from which a model must learn and make predictions on new examples. As such, it is a learning problem that sits between supervised learning and unsupervised learning. We require semi-supervised learning algorithms when working with data where labeling examples is challenging or expensive. The sign of an effective semi-supervised learning algorithm is that it can achieve better performance than a supervised learning algorithm fit only on the labeled training examples.
Semi-supervised learning algorithms generally are able to clear this low bar expectation. Finally, semi-supervised learning may be used or may contrast inductive and transductive learning.
Generally, inductive learning refers to a learning algorithm that learns from labeled training data and generalizes to new data, such as a test dataset. Transductive learning refers to learning from labeled training data and generalizing to available unlabeled (training) data. Both types of learning tasks may be performed by a semi-supervised learning algorithm.