Supervised learning is a type of machine learning where a function is inferred from labeled training data. The training data contains a set of training examples. Unsupervised learning, on the other hand, is a type of machine learning where inferences are drawn from datasets containing input data without labeled responses. Following are the various other differences between the two types of machine learning:
Algorithms Used – Supervised learning makes use of Decision Trees, K-nearest Neighbor algorithm, Neural Networks, Regression, and Support Vector Machines. Unsupervised learning uses Anomaly Detection, Clustering, Latent Variable Models, and Neural Networks.
Enables – Supervised learning enables classification and regression, whereas unsupervised learning enables classification, dimension reduction, and density estimation
Use – While supervised learning is used for prediction, unsupervised learning finds use in analysis