How do you extend logistic regression to more than two classes?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
One-vs-rest fits a binary model per class.
WHY — Logistic Regression instead of guessing?
Why interviewers care about Logistic Regression:
question about Logistic Regression.
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:
- 1One-vs-rest fits a binary
model per class.
- 2Multinomial logistic regression, or
softmax regression, models all classes in one likelihood.
- 3How it works
sklearn exposes both
- 4multinomial is usually preferred
when classes are mutually exclusive.
- 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
One-vs-rest fits a binary model per class. Multinomial logistic regression, or softmax regression, models all classes in one likelihood.