True or false: k-Means is a non-predictive, unsupervised learning method.

• A. True
• B. False
• C. Not determinable
• D. Both true and false

Answer: (A)

k-Means is an unsupervised clustering algorithm that groups data into k clusters based on similarity, using only the feature values without any target variable. Since there is no label or outcome to predict, it doesn’t learn a mapping from inputs to a target and is therefore non-predictive. In unsupervised learning, the goal is to discover structure in the data itself rather than predict a specific outcome, which is exactly what k-Means does by forming clusters. Some might assign new points to the nearest cluster centroid, but that is not creating a supervised predictive model; it’s just labeling based on existing clusters. So the statement is true.