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Supervised vs unsupervised learning
Short answer
Supervised learning uses labeled data to predict outputs (e.g., spam detection), while unsupervised learning finds patterns in unlabeled data (e.g., customer segmentation). Supervised tasks include classification and regression; unsupervised includes clustering and association.
Differentiate supervised and unsupervised learning with suitable examples.
Supervised learning learns from training data that contains the correct output (class label) for each input, and builds a model to predict labels for new data. Unsupervised learning works on data without labels and finds hidden structure or groups by itself.
| Basis | Supervised learning | Unsupervised learning |
|---|---|---|
| Training data | Labelled (input and known output) | Unlabelled (input only) |
| Goal | Predict a class or value | Discover patterns and groups |
| Supervision | A "teacher" gives the correct answer | None |
| Main tasks | Classification, regression | Clustering, association, dimensionality reduction |
| Evaluation | Accuracy on test set, easy to measure | Harder, uses cluster quality measures |
| Number of classes | Known in advance | Not known (or chosen, e.g. k) |
| Algorithms | Decision tree, Naive Bayes, KNN, SVM, neural network | k-means, hierarchical, DBSCAN, Apriori, PCA |
| Example | Predict if an email is spam from emails already marked spam/not spam | Group customers into segments by buying behaviour |
A third type, semi-supervised learning, uses a few labelled and many unlabelled examples.
✓ Answer checked by Claude Opus 5.5 (high effort)
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