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Understanding PCA in Unsupervised Learning by Suchismita Das is a document available to read on EtoBox.

Unsupervised learning involves analyzing data without labeled responses. The goals are to discover subgroups, reduce dimensionality for visualization, and find interesting patterns. Principal component analysis (PCA) is an unsupervised technique that finds linear combinations of variables with maximal variance. PCA produces principal components that can be used to visualize high-dimensional data in 2D or 3D spaces.

Author

Suchismita Das

Language

EN