How integrate unsupervised methods into our pipeline?
Combining unsupervised and supervised unlocks deeper insights.
How It Works:
Use embeddings from autoencoders or clustering to preprocess data, then feed structured features into supervised models-or detect data drift and anomalies in production.
Key Benefits:
- Enhances downstream model performance
- Automates data exploration
- Flags unusual inputs proactively
Real-World Use Cases:
- Fraud detection with anomaly scores
- Feature learning for recommendation engines
FAQs
Can unsupervised replace labeling?
How scale cluster updates?
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