Ferry-terminal logs have hour, weather, and passenger counts, but nobody tagged busy versus quiet. Staff want natural groups of similar terminals. Which approach fits?
Select an answer to reveal the explanation.
Short Explanation
Think of ferry logs with hour, weather, and counts, and nobody tagged busy versus quiet. Staff want natural groups. SageMaker K-Means. Inventing a busy/quiet label for XGBoost is a different problem.
Full Explanation
Unsupervised clustering, such as SageMaker K-Means, is the fit when staff want groups and there is no target column. Inventing a busy/quiet label for XGBoost turns the problem into a classifier. DeepAR forecasts related series. Translate is not clustering.