Debug a model with overfitting
Last updated: January 14, 2026
Quick Overview
Your model shows poor recall. Walk through your debugging process and potential fixes.
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Your model shows poor recall. Walk through your debugging process and potential fixes.
Machine learning questions at LinkedIn test both theoretical understanding and practical experience. This Onsite 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
- What regularization technique would you use and why?
- How would you handle a highly imbalanced dataset?
- How would you ensure reproducibility in your ML pipeline?
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Explore ML Interview PrepSample Answer
Core Concept: Overfitting in Machine Learning
Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor generalization on unseen data. This typically results in high accuracy on the train...
How It Works: Debugging Overfitting
To debug a model with overfitting, I would first analyze the learning curves by plotting the training and validation losses over epochs. If the training loss decreases while the validation loss increa...
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