A bank needs to build a SageMaker AI model to decide customer eligibility for a new product using algorithms that SageMaker directly supports. The model must be explainable to regulators. Which modeling choice satisfies these requirements?

The Linear Learner algorithm is a supervised learning algorithm that supports both regression and classification. It is known for its interpretability, making it suitable for scenarios where model explainability is crucial, such as regulatory compliance. This algorithm directly supports SageMaker, fulfilling the requirements. Object2Vec is an unsupervised algorithm for learning embeddings, not directly for classification or regression on tabular data for eligibility. Neural networks, while powerful, are generally considered "black box" models due to their complex internal workings, making them difficult to explain to regulators. K-means is an unsupervised clustering algorithm, not designed for predicting eligibility (a supervised classification task).