Data Scientist
What is overfitting, and how would you spot it in a model you just trained?
What the interviewer is looking for
- This question tests predictive modelling. Chooses models for the problem and constraints, explains overfitting and regularisation, and justifies complexity with measurable gains.
- Strong answers: Model learns noise instead of patterns, detected by a gap between training and validation scores, fixed with simpler models, regularisation or more data.
- Likely follow-up: How does cross validation help here?
Another way it may be asked: Your model scores almost perfectly on training data but poorly on new data. What is going on and how would you fix it?
A structure to follow: Answer, explain, example, limits
- Step 1
Answer
Give your direct answer in the first sentence so the interviewer knows where you are going.
- Step 2
Explain
Say how it works or why you would do it that way, in plain words before jargon.
- Step 3
Example
Anchor it in something you have actually done or seen, with the detail that shows hands-on experience.
- Step 4
Limits
Say when your approach would not be the right one. It shows judgement.
This is guidance on shape, not a script. Use your own real experience and facts; never invent details to fill a gap. See the STAR guide and answer frameworks.
Common slips
- Reciting a definition without showing you have applied it.
- Using terms you cannot explain if asked a level deeper.
- Claiming experience beyond what you can discuss in detail.
How to practise it
Say the answer in 90 seconds, then answer the follow-up as if it were a new question.
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