Which machine learning technique is typically used for predicting house prices?

The correct answer, regression, is a machine learning technique specifically designed for predicting continuous outcomes, such as house prices. In this context, regression models analyze historical data where the target variable, house prices, can be influenced by various features such as square footage, number of bedrooms, location, and other relevant attributes. The goal of regression is to find the relationship between these features and the house prices, allowing predictions on pricing for new inputs.

This approach contrasts with classification, which is suited for predicting categorical outcomes rather than continuous values like house prices. Reinforcement learning focuses on training agents to make decisions by receiving rewards or penalties for actions taken in specific environments, making it unsuitable for direct price prediction tasks. Unsupervised learning involves grouping or organizing data without predefined labels, which does not apply to a task where specific outcomes are known and being predicted, like house prices. Thus, regression stands out as the most appropriate technique for this scenario.