Which learning paradigm involves learning from labeled data to categorize data or predict numerical values?

Which learning paradigm involves learning from labeled data to categorize data or predict numerical values?

• A. Unsupervised Learning
• B. Supervised Learning
• C. Reinforcement Learning
• D. Semi-supervised Learning

Answer: (B)

Supervised learning involves learning from labeled data to create a function that maps inputs to outputs. With labeled examples, the model learns to categorize data into classes or to predict numerical values (regression). The labels provide ground-truth targets that guide the learning process, typically by minimizing the difference between predicted and true values. This approach contrasts with unsupervised learning, which uses only input data to discover structure; reinforcement learning, where an agent learns from rewards through interaction with an environment; and semi-supervised learning, which uses mostly unlabeled data with a subset of labeled data to guide learning. For example, labeling emails as spam or not helps classify new emails, while labeling houses with their prices enables accurate price predictions.

Supervised learning involves learning from labeled data to create a function that maps inputs to outputs. With labeled examples, the model learns to categorize data into classes or to predict numerical values (regression). The labels provide ground-truth targets that guide the learning process, typically by minimizing the difference between predicted and true values. This approach contrasts with unsupervised learning, which uses only input data to discover structure; reinforcement learning, where an agent learns from rewards through interaction with an environment; and semi-supervised learning, which uses mostly unlabeled data with a subset of labeled data to guide learning. For example, labeling emails as spam or not helps classify new emails, while labeling houses with their prices enables accurate price predictions.