Used when data classes are not separated, such as when the data is continuous.

• A. Classification
• B. Clustering
• C. Regression
• D. Dimensionality Reduction

Answer: (C)

Regression is used when the target outcome is numeric and can take on a continuum of values. If the data don’t form distinct categories and you want to predict a real-valued quantity (for example, house price, temperature, or sales), a regression model learns the relationship between the input features and that continuous target. It produces a numeric prediction rather than assigning a label to a category. Other techniques like classification predict discrete classes, clustering groups data without labels, and dimensionality reduction focuses on simplifying features rather than directly predicting a numeric outcome. So when the data are continuous, regression is the best fit for estimating the actual value.