Reference · Glossary
Supervised learning
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Machine learning from **labeled examples** — each input comes with the correct answer the model should learn to predict.
When to use
Spam vs not spam, cat vs dog photos, sentiment labels, fraud yes/no — whenever you have trustworthy labels at scale.
When not to
When labels are missing, expensive, or wrong — consider unsupervised clustering or semi-supervised approaches instead.
Example
10,000 emails marked "spam" or "not spam" → train a classifier → flag new inbox messages automatically.