Debug a model with data leakage
Last updated: October 31, 2025
Quick Overview
Your model shows poor recall. Walk through your debugging process and potential fixes.
Zillow
October 31, 202535
8
80 solved
Your model shows poor recall. Walk through your debugging process and potential fixes.
Machine learning questions at Zillow 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 detect and handle concept drift?
- How would you handle a highly imbalanced dataset?
- What are the computational costs of this approach at scale?
- When would you prefer a simpler model over a complex one?
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Explore ML Interview PrepSample Answer
Core Concept: Understanding Data Leakage
Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance metrics during training and poor generalization to unseen d...
How it Works: Identifying Data Leakage
To identify and debug data leakage, I would first inspect the data preprocessing pipeline. This involves checking for features that may have been created using information from the target variable or ...
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