Explain regularization and its applications
Last updated: February 8, 2026
Quick Overview
Describe regularization in depth, including how it works, when to use it, and pitfalls.
Walmart
Machine Learning
Machine Learning Engineer
Walmart
February 8, 2026Machine Learning Engineer
Technical Screen
Machine Learning
Medium
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4,138 solved
Describe regularization in depth, including how it works, when to use it, and pitfalls.
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.
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