See Unsupervised Learning as Pattern Finding
Explain when unsupervised learning is useful and what it can and cannot tell a team.
The reframe: unsupervised learning is for finding structure before the organization has a settled answer key. No label, different job When no target outcome exists, the model cannot train by comparing predictions with a right answer. Instead, it searches for similarities, clusters, or lower-dimensional structure in the data. Useful for exploration, not automatic truth Clusters can reveal recurring shapes in a dataset: types of tickets, groups of buyers, or unusual behavior patterns. But the model does not know whether those groups are strategically meaningful. Humans must inspect and name them. Your feature choices still shape the result If you feed…
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