Supervised Learning
Supervised learning is a type of machine learning where the algorithm learns from labelled training data — input-output pairs. The model learns to map inputs to correct outputs, then makes predictions…
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Definition
Supervised learning is a type of machine learning where the algorithm learns from labelled training data — input-output pairs. The model learns to map inputs to correct outputs, then makes predictions on new, unseen data. It's the most common and practical type of ML, powering applications from spam detection to medical diagnosis.
Key Points
- Requires labelled data — each example has a known correct answer
- Two types: classification (categorise) and regression (predict numbers)
- Common algorithms: linear regression, decision trees, random forests, gradient boosting, neural networks
- Most business ML applications use supervised learning
Related Terms
Frequently Asked Questions
Classification predicts categories: Is this email spam or not? What disease does this patient have? Regression predicts numbers: What will tomorrow's sales be? How much should this property cost? Both are supervised learning — the difference is whether the output is a category or a continuous number.
It depends on the problem complexity. Simple classification: 100-1,000 samples per class. Complex tasks: 10,000+ samples. Deep learning: often 100,000+. For Indian businesses with limited data, techniques like transfer learning, data augmentation, and few-shot learning can reduce data requirements significantly.
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