Answer: Supervised learning

## Explanation **Correct Answer: B) Supervised learning** **Why this is correct:** 1. **Labeled Data**: The problem explicitly mentions "historical labeled data" where customers are labeled as "default" or "non-default." This is the key characteristic of supervised learning. 2. **Classification Task**: Predicting whether a customer will default or not is a binary classification problem, which falls under supervised learning. 3. **Training Process**: In supervised learning, the model learns from labeled examples (input-output pairs) to make predictions on new, unseen data. **Why other options are incorrect:** - **A) Unsupervised learning**: This is used when there are no labels in the data. The algorithm tries to find patterns or structure in the data without predefined categories. - **C) Transfer learning**: This involves using knowledge gained from solving one problem to help solve a different but related problem. While it can be used with supervised learning, it's not the primary method for this specific scenario. - **D) Reinforcement learning**: This involves an agent learning through trial and error by interacting with an environment and receiving rewards or penalties. It's not suitable for this classification problem with historical labeled data. **Key Takeaway**: When you have labeled historical data and want to predict outcomes based on that data, supervised learning is the appropriate machine learning approach.

Author: Jin H