Which statement best describes supervised learning?

• A. Unsupervised learning uses labeled data to predict outputs.
• B. Reinforcement learning learns from rewards through interactions with an environment.
• C. Supervised learning uses labeled data to predict outputs.
• D. Unsupervised learning uses unlabeled data to discover patterns.

Answer: (C)

Supervised learning hinges on labeled data paired with the correct outputs. You provide examples where each input has an associated target value, and the algorithm learns a function that maps inputs to those targets. The model is then used to predict outputs for new, unseen inputs, aiming to minimize the difference between predicted and true labels. This reliance on labeled data to predict outputs is what makes this description fit supervised learning best. The other approaches differ: unsupervised learning uses unlabeled data to discover patterns or structure, and reinforcement learning learns through rewards obtained from interactions with an environment.