Unsupervised Learning
Machine learning approach where the model learns patterns from unlabeled data without predefined target outputs.
In more detail
Unsupervised learning finds hidden patterns and structures in data. Key techniques include clustering (grouping similar items), dimensionality reduction (reducing feature count), and anomaly detection (finding outliers). Useful when labeled data is scarce or when exploring data structure.
Examples
- Customer segmentation
- Anomaly detection for fraud
- Dimensionality reduction with PCA
- Text topic modeling
Related terms
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