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

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.

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.