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Prevent Overfitting in ML Models

EasyMachine Learning00:00
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Welcome to your interview.

The question is on your right: Prevent Overfitting in ML Models. Take a moment with it first.

Talk your thinking through with me if you like - when you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes). Discussion and graded submissions share your five interviewer interactions, so spend them well.

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Problem

You have a model that fits the training set too well and does not generalize. The goal is to explain the practical ways to reduce overfitting and choose the right level of model complexity.

What to look for

  • Large gap between training and validation metrics
  • High variance across cross-validation folds
  • Performance that drops when the model sees new data