Supervised vs Unsupervised Learning Explained
Supervised learning trains on labelled examples to make predictions; unsupervised learning finds structure in unlabelled data, like grouping similar items.
Quick version: supervised learning predicts from labelled examples; unsupervised learning finds patterns without labels.
| Type | Data | Typical tasks |
|---|---|---|
| Supervised | Labelled | Classification, regression, forecasting |
| Unsupervised | Unlabelled | Clustering, anomaly detection, segmentation |
Most business prediction problems — churn, demand, pricing — are supervised. Unsupervised methods shine for exploration, like customer segmentation. Many real systems combine both.
Frequently asked questions
What is the difference between supervised and unsupervised learning?
Supervised learning trains on labelled examples to predict a known outcome (like spam or not-spam). Unsupervised learning works with unlabelled data to discover structure, such as grouping similar customers.
When do you use unsupervised learning?
When you lack labels and want to explore structure — for example, segmenting customers, detecting anomalies, or reducing data complexity.
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