Problem
Scenario
You're training a supervised learning model and notice that training performance is strong, but validation performance is much weaker. You need to improve generalization without losing too much signal.
Question
How would you handle overfitting in a predictive model?
What This Tests
- Diagnosing overfitting from train versus validation behavior
- Using regularization to control model complexity
- Applying cross-validation correctly
- Tuning hyperparameters with the bias-variance tradeoff in mind
Practicing as: Data Scientist interview at AlteryxHi, I'll play your Alteryx interviewer for the Data Scientist role. Candidates describe these interviews as often stressful and moderately difficult, so expect me to be direct and to the point. Take your time with the question above and answer like we're in the room.
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