Unsupervised learning: Difference between revisions
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'''Unsupervised learning''' is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between [[supervised learning]] and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than [[supervised learning]]. Its goal is to automatically find structure in a dataset. | '''Unsupervised learning''' is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between [[supervised learning]] and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than [[supervised learning]]. Its goal is to automatically find structure in a dataset, to find the regularities in the input, to see what normally happens. | ||
== Unsupervised learning techniques == | == Unsupervised learning techniques == | ||
Revision as of 19:13, 23 March 2020
Unsupervised learning is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between supervised learning and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than supervised learning. Its goal is to automatically find structure in a dataset, to find the regularities in the input, to see what normally happens.