What does supervised learning do?

• A. Algorithm uses data to learn relationship between inputs and outputs
• B. Algorithm learns relationships without labeled data
• C. Algorithm requires manual rules
• D. It cannot learn from data

Answer: (A)

Supervised learning builds a model from data that includes both inputs and the correct outputs. The algorithm learns a function that maps inputs to outputs by studying examples where the true result is known. It predicts an output for each input and adjusts its parameters to reduce the difference between the predicted and true outputs. In effect, it learns the relationship between the input features and the target variable so it can make accurate predictions on new data. For instance, predicting house prices from features like size and location uses labeled data (actual prices) to learn how those features relate to price. The other statements describe scenarios that aren’t how supervised learning works: learning without labeled data aligns with unsupervised learning, relying on manual rules describes a rule-based system, and saying it cannot learn from data is incorrect.