Problem
Scenario
You're training a supervised learning model and notice it performs much better on the training set than on validation data. You want to improve generalization without throwing away useful signal.
Question
How would you handle overfitting in a model?
What This Tests
- Recognizing overfitting from train versus validation behavior
- Using regularization and model complexity control
- Applying cross-validation correctly
- Tuning hyperparameters to improve generalization
Practicing as: Machine Learning Engineer interview at Colgate-PalmoliveHi, I'll play your Colgate-Palmolive interviewer for the Machine Learning Engineer role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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