Question 23
Domain 3: Train and evaluate modelsTrue or False: A major difference between clustering and classification models is that clustering is a supervised method, where training is done with labels.
Correct answer: B
Explanation
Clustering is an unsupervised learning method, so it groups data based on similarity without labeled examples. Classification is the supervised method because it is trained on labeled data to predict known classes. Thus, saying clustering is supervised and uses labels is false.
Why each option is right or wrong
A. Yes. Clustering uses labeled examples to learn the correct output classes.
B. No. Clustering is typically an unsupervised method and does not require labeled training data.
Clustering is governed by unsupervised learning, meaning the algorithm infers structure from the feature space without target labels; there is no training set of labeled examples to fit against. By contrast, classification is the supervised case, where the model is trained on labeled outcomes to learn a decision boundary and predict a known class for new observations.