Explain model pruning and its applications
Last updated: September 2, 2025
Quick Overview
Describe model pruning in depth, including how it works, when to use it, and common pitfalls.
Amazon
September 2, 2025183
6
1,669 solved
Describe model pruning in depth, including how it works, when to use it, and common pitfalls.
This ML question from Amazon's Technical Screen goes beyond textbook definitions. The interviewer wants to see how you reason about model selection, evaluation metrics, and the practical challenges of deploying ML in production.
What the Interviewer Expects
- Explain the concept clearly with intuitive examples
- Discuss when and why to use this technique
- Identify common pitfalls and how to avoid them
- Compare with alternative approaches at a high level
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you ensure reproducibility in your ML pipeline?
- How would you handle a highly imbalanced dataset?
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Core Concept Explanation
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Practical Application
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