How do L1 and L2 regularization differ?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
L2, also called ridge, shrinks weights toward zero but rarely makes them exact zeros.
WHY — Regularization instead of guessing?
Why interviewers care about Regularization:
question about Regularization.
trade-offs, and what you would actually do on a AI / ML project - not buzzwords.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1L2, also called ridge,
shrinks weights toward zero but rarely makes them exact zeros.
- 2L1, also called lasso,
can zero out weights and drop features.
- 3Elastic net mixes the
two penalties.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
L2, also called ridge, shrinks weights toward zero but rarely makes them exact zeros. L1, also called lasso, can zero out weights and drop features.