Question: A type of machine learning uses unlabeled datasets to allow a program to identify patterns in data without a specific output in mind. Which approach to machine learning does this scenario describe?
- Supervised learning
- Reinforcement learning
- Unsupervised learning
Explanation
Unsupervised learning uses data without labeled outputs to discover structure or patterns. This approach is useful when the goal is exploration rather than matching inputs to predefined answers. Common outcomes include grouping similar items, finding relationships, or detecting unusual patterns. It differs from methods that rely on examples with known labels or reward-based feedback.
Why the other options are incorrect
Supervised learning is incorrect because it uses labeled data with known outputs.
Reinforcement learning is incorrect because it learns through actions, feedback, and rewards.
Source for verification
https://developers.google.com/machine-learning/clustering/overview
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