Debug a model with overfitting
Last updated: April 25, 2026
Quick Overview
Your model shows high variance. Walk through your debugging process and potential fixes.
Waymo
April 25, 20268
5
2,326 solved
Your model shows high variance. Walk through your debugging process and potential fixes.
Machine learning questions at Waymo test both theoretical understanding and practical experience. This Take-home Project question evaluates your knowledge of ML fundamentals and your ability to apply them to real-world problems.
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 ensure reproducibility in your ML pipeline?
- What are the computational costs of this approach at scale?
- What regularization technique would you use and why?
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Explore ML Interview PrepSample Answer
Core Concept: Overfitting in Machine Learning
Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading to high variance. In the context of Waymo, where models predict vehicle behavio...
How It Works: Mechanisms Behind Overfitting
Mathematically, overfitting can be understood through the lens of bias-variance tradeoff. A model with high capacity (like deep neural networks) can fit the training data perfectly, leading to a low t...
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