Semi-supervised learning is an approach to machine learning which uses some labeled data and some data without labels to train models. This approach can be useful to overcome the problem of insufficient quantities of labeled data. Some consider it to be a variation of supervised learning, whilst other authors consider it to lie somewhere between supervised and unsupervised learning; however this categorization issue is academic. Such an approach has been applied to many types of problems including regression and classification problems.
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