Unsupervised learning is a type of machine learning where you do not have ground truth labels. Algorithms must find patterns, groupings, or structures in the data on their own. Key tasks include clustering, dimensionality reduction, and anomaly detection.

Evaluating unsupervised models is more challenging than supervised ones because you cannot directly compare predictions to known answers. You must use alternative strategies and metrics to judge model quality.

Evaluating unsupervised models involves trade-offs, especially in interpretability and reliance on the data's internal structure. Some metrics are clear and explainable, while others are more technical or abstract.

Metric choice depends on your task:

• In clustering, use metrics that assess cluster compactness and separation;
• For dimensionality reduction, focus on how much information is preserved;
• In anomaly detection, measure how well rare cases are distinguished from normal data.

Selecting the right metric is essential, as it shapes your understanding of model quality and supports your specific goals.