Question: A machine learning program uses a training dataset containing information, known as labels. Which approach to machine learning does this scenario describe?
- Unsupervised learning
- Reinforcement learning
- Supervised learning
Explanation
Supervised learning uses labeled data during training so a model can learn relationships between inputs and known outputs. Labels provide the expected answers that guide the model as it identifies patterns. This approach is commonly used for prediction or classification tasks where the desired output is already defined. The model improves by comparing its predictions with the labeled examples.
Why the other options are incorrect
Unsupervised learning is incorrect because it uses unlabeled data to identify patterns without predefined outputs.
Reinforcement learning is incorrect because it learns through actions, feedback, and rewards rather than labeled examples.
Source for verification
https://cloud.google.com/learn/what-is-machine-learning
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