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Making use of both unsupervised and supervised learning paradigms to train on a partially labelled dataset.

In a partly labeled dataset, using only the labeled observations to train a model can prove non-optimal. The remaining, unlabeled part of the dataset may contain valuable information about data structure, that could be used to improve the model, especially when the proportion of labeled data is low.

The semi-supervised learning approach uses both unsupervised learning and supervised learning concepts in order to get the best from a dataset. This paradigm includes specific semi-supervised techniques as well as mixed-up approaches using standard supervised and unsupervised methods.