The discriminant analysis is a ______ model.

• A. Regression
• B. Classification
• C. Prediction
• D. Clustering

Answer: (C)

Discriminant analysis is a classification approach. It uses labeled data to learn how the predictor variables behave within each predefined class and then assigns new observations to the class with the highest likelihood (or posterior probability). This creates decision boundaries (linear for linear discriminant analysis, quadratic for quadratic discriminant analysis) that separate the classes. It’s a supervised technique, unlike clustering, which groups data without labels, and it isn’t about predicting a numeric value like regression. While you do obtain a predicted class label for new cases, the fundamental purpose is to classify observations into predefined categories.