AWS Certified Cloud Practitioner
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A bank wants to build a credit-risk model using historical labeled data — customers are labeled as "default" or "non-default." Which ML method should be applied?
Real Exam
Community
RRitesh
0
A
Unsupervised learning
B
Supervised learning
C
Transfer learning
D
Reinforcement learning
Explanation:
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
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