Algorithms may cluster examples, estimate a distribution, reduce dimensionality, or learn representations from relationships within the input. Evaluation depends on whether the discovered structure is useful for a defined downstream purpose.
Unsupervised learning finds structure in data without relying on a provided target label for each example.
Algorithms may cluster examples, estimate a distribution, reduce dimensionality, or learn representations from relationships within the input. Evaluation depends on whether the discovered structure is useful for a defined downstream purpose.