When is elastic net better than pure lasso or ridge?
PICTURE THIS: HOW TO EXPLAIN IT
IdeaRegularization
HowWhat happens inside
Why they askShows real use
Simple meaning
Elastic net mixes L1 and L2.
WHY — Regularization instead of guessing?
Why interviewers care about Regularization:
They are checking judgment
on Regularization.
A good answer names
the situation, the default choice, and one exception - that reads as experience.
Stay structured
Name the idea, why it exists, then one short example.
Close cleanly
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:
- 1Elastic net mixes L1
and L2.
- 2Lasso is unstable when
features are highly correlated
- 3How it works
ridge keeps all of them.
- 4The mix can select
groups more gracefully while still shrinking coefficients.
- 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:
Say this line
“Lasso is unstable when features are highly correlated”
Break into beats
Lassoisunstablewhenfeaturesare
Speaking order
2987408337471632900
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Elastic net mixes L1 and L2. Lasso is unstable when features are highly correlated