What is the difference between interpolation and generalization?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
Interpolation means the model can fit the training points, even perfectly.
WHY — Overfitting instead of guessing?
Why interviewers care about Overfitting:
contrast on Overfitting, not two memorised paragraphs.
the developer, then one case where picking wrong hurts.
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:
- 1Interpolation means the model
can fit the training points, even perfectly.
- 2Generalization means it still
predicts well on new points from the same process.
- 3Modern nets can interpolate
and still generalize, so zero training loss is not proof of failure or success by itself.
- 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
Interpolation means the model can fit the training points, even perfectly. Generalization means it still predicts well on new points from the same process.