What is regularization and how does it prevent overfitting?
This question probes your knowledge of optimization techniques used to control model complexity and improve generalization capabilities.
Why Interviewers Ask This
Regularization is a core concept in preventing models from becoming too specialized to training data. Interviewers ask this to ensure you understand the mathematical intuition behind adding penalties to loss functions. It demonstrates your grasp of how to balance fitting the data against keeping the model simple.
How to Answer This Question
Explain that regularization adds a penalty term to the loss function to discourage large weights. Describe how this reduces model complexity and prevents the model from relying too heavily on specific features. Mention specific types like L1 and L2 and their distinct effects on weight distribution.
Key Points to Cover
- Regularization adds a penalty to the loss function.
- It reduces model complexity by shrinking weights.
- It improves generalization on unseen datasets.
- Different types offer varying degrees of feature selection.
Sample Answer
Regularization is a technique used to reduce model complexity and prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from assigning excessively large weights to specific…
Common Mistakes to Avoid
- Failing to mention the penalty term mechanism.
- Not distinguishing between L1 and L2 effects.
- Claiming regularization increases training accuracy.
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