Supervised Learning vs Unsupervised Learning

Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. In classification models, the output is discrete. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. Clustering is an unsupervised technique that involves the grouping, or clustering, of data points.