Unsupervised Learning is a machine learning paradigm where models learn patterns and relationships from unlabeled data without specific output labels. It focuses on finding hidden structures and patterns in data to make inferences and discover insights.
- Glossary > Letter: U
What does "Unsupervised Learning" mean?
Use Cases
Clustering:
Grouping similar data points into clusters based on patterns or features.
Anomaly Detection:
Identifying unusual patterns or outliers in data without prior labels.
Dimensionality Reduction:
Reducing the number of variables in data while preserving important information.
Importance
Exploratory Analysis:
Provides insights into data structure and relationships for further analysis.
Scalability:
Scales well with large datasets where labeled data may be scarce or costly.
Feature Discovery:
Uncovers hidden patterns and structures that may not be apparent through supervised methods.
Analogies
Unsupervised Learning is like exploring a new city without a map or guide. Just as you discover patterns and locations by exploring streets and neighborhoods without predefined directions, unsupervised learning discovers patterns and relationships in data without labeled examples.
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