A publisher wants to build a targeted marketing classifier that predicts subscription status from a table with numeric and categorical features (for example, age and education). Which Amazon SageMaker built-in algorithm is most suitable for this supervised classification task?

XGBoost is a highly effective and widely used algorithm for supervised classification tasks, especially with tabular data containing both numeric and categorical features. It's an optimized distributed gradient boosting library designed for speed and performance. Random Cut Forest (RCF) is an unsupervised algorithm primarily used for anomaly detection, not classification. Neural Topic Model (NTM) is an unsupervised algorithm for discovering latent topics in text documents. DeepAR forecasting is a supervised algorithm specifically for time-series forecasting, not general classification.