Explanation

This is a supervised learning problem because:

• Labeled Data: The bank has historical data where customers are explicitly labeled as "default" or "non-default"
• Classification Task: The goal is to predict categorical outcomes (default vs non-default) based on input features
• Training Process: The model learns from labeled examples to make predictions on new, unseen data

Why Other Options Are Incorrect:

• A) Unsupervised learning: Used when data has no labels and the goal is to find patterns or groupings
• C) Transfer learning: Involves using knowledge from one domain to improve learning in another domain
• D) Reinforcement learning: Focuses on learning through trial-and-error interactions with an environment to maximize rewards

Key Characteristics of Supervised Learning:

• Requires labeled training data
• Used for classification (categorical outcomes) and regression (continuous outcomes)
• Common algorithms: Logistic Regression, Decision Trees, Random Forests, Neural Networks
• Perfect for credit risk assessment where historical outcomes are known