Explain overfitting and how to prevent it

You'll face a rapid-fire round of core machine learning questions.

1. What is overfitting? Describe it using training versus validation performance and the idea of generalization.
2. How do you prevent or reduce overfitting? Name practical methods and the situations where each is appropriate.
3. How do L1 and L2 regularization differ? Contrast their impact on model weights, feature selection, optimization behavior, and common applications.

Overview: This question tests your grasp of overfitting, how training and validation performance relate to generalization, and regularization concepts, including the distinctions between L1 and L2 penalties.