What is R-squared and what can it hide?
PICTURE THIS: OVERFITTING
Simple meaning
R-squared is the fraction of variance in y explained by the model.
WHY — Linear Regression instead of guessing?
Why interviewers care about Linear Regression:
people who only read docs from people who shipped.
and tied to AI / ML work.
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:
- 1R-squared is the fraction
of variance in y explained by the model.
- 2It never falls when
you add features, so adjusted R-squared or out-of-sample R-squared is safer.
- 3A high R-squared can
still sit on biased residuals or a useless business error.
- 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
R-squared is the fraction of variance in y explained by the model. It never falls when you add features, so adjusted R-squared or out-of-sample R-squared is safer.