Which type of machine learning is trained by providing explicit examples of desired results, such as defective vs non-defective items?

Which type of machine learning is trained by providing explicit examples of desired results, such as defective vs non-defective items?

• A. Unsupervised learning
• B. Reinforcement learning
• C. Supervised learning
• D. Agentic AI

Answer: (C)

Providing explicit examples of desired results means you give the model labeled data—each input paired with the correct output. This defines supervised learning, where the algorithm learns a mapping from features to labels by learning from those labeled examples. For a task like distinguishing defective from non-defective items, you label each item as defective or not, train on those labeled examples, and then the model predicts the label for new items. Unsupervised learning uses unlabeled data to find structure without correct outputs, reinforcement learning learns by taking actions and receiving rewards, and agentic AI isn’t a standard category in this context.

Providing explicit examples of desired results means you give the model labeled data—each input paired with the correct output. This defines supervised learning, where the algorithm learns a mapping from features to labels by learning from those labeled examples. For a task like distinguishing defective from non-defective items, you label each item as defective or not, train on those labeled examples, and then the model predicts the label for new items. Unsupervised learning uses unlabeled data to find structure without correct outputs, reinforcement learning learns by taking actions and receiving rewards, and agentic AI isn’t a standard category in this context.