Unsupervised learning is a machine learning paradigm where models are trained on datasets without labeled outputs. Instead of learning from specific input-output pairs, the algorithm identifies patterns and structures within the data. This approach is widely used for clustering, anomaly detection, and dimensionality reduction.
- Discover Patterns: Unsupervised learning can uncover hidden structures in data that may not be immediately apparent.
- Data Exploration: It is useful for exploratory data analysis, allowing insights without prior labels or categories.
- Feature Reduction: Techniques like Principal Component Analysis (PCA) help simplify data while retaining essential information.
- Interpretability: The results can be harder to interpret compared to supervised learning since there are no clear labels to guide the understanding.
- Quality of Data: The performance heavily relies on the quality and nature of the input data, making it sensitive to noise.
- Evaluation Metrics: Measuring the success of unsupervised learning can be challenging, as there are no ground truth labels to compare against.