A company must use a SageMaker built-in algorithm to rank customers by their likelihood of repaying loans. Which built-in algorithm should be used for this supervised ranking/classification task?

XGBoost is a powerful, scalable, and widely used gradient boosting algorithm that excels in supervised learning tasks like classification and ranking. For ranking customers by their likelihood of repaying loans, this algorithm can effectively learn complex relationships between customer features and repayment probability, making it suitable for this binary classification problem (repay/not repay) which can then be used for ranking. K-means clustering is an unsupervised learning algorithm used for grouping similar data points, not for classification or ranking. Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique, not a classification algorithm. Neural Topic Model (NTM) is an unsupervised algorithm for discovering abstract "topics" in a collection of documents, unrelated to customer loan repayment prediction.