Your question is Handling Overfitting in Predictive Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
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.
How would you handle overfitting in a predictive model?