In a complex financial modeling project, you are tasked with predicting the likelihood of a customer defaulting on a loan based on various financial attributes such as income, debt, and credit history. What type of machine learning problem is this, and what are the key considerations in choosing an appropriate approach?

Group of answer choices

Classification, considering the binary nature of default prediction. Key considerations include model interpretability and handling imbalanced datasets.

Reinforcement Learning, as the model needs to learn optimal strategies for managing loan portfolios. Key considerations include reward structures and exploration$-$exploitation trade$-$offs.

Clustering, as the goal is to group customers with similar financial behaviors. Key considerations include distance metrics and cluster evaluation.

Regression, considering the continuous nature of financial attributes. Key considerations include feature engineering and regularization techniques.