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Explain supervised, unsupervised, and reinforcement learning.
1 Answer
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1. Supervised Learning
The model learns from labeled data, where the correct output is already known.
Example:
- Predicting house prices
- Email spam detection
Input:
Features + Correct Answers
Goal: Learn a mapping from inputs to outputs.
2. Unsupervised Learning
The model learns from unlabeled data and tries to find hidden patterns or groups.
Example:
- Customer segmentation
- Market basket analysis
Input:
Features Only (No Answers)
Goal: Discover structure, clusters, or relationships in the data.
3. Reinforcement Learning
An agent learns by interacting with an environment and receiving rewards or penalties for its actions.
Example:
- Game-playing AI
- Self-driving cars
- Robot navigation
Process:
Action → Reward/Penalty → Learn → Improve
Goal: Maximize cumulative rewards over time.
| Learning Type | Data | Goal | Example |
|---|---|---|---|
| Supervised | Labeled | Predict outcomes | Spam detection |
| Unsupervised | Unlabeled | Find patterns | Customer clustering |
| Reinforcement | Rewards/Penalties | Learn best actions | Game AI |
