How can I avoid overfitting when tuning parameters?

What are some strategies to avoid overfitting when I'm tuning my model's parameters?

To avoid overfitting, you can use techniques like cross-validation, regularization, and pruning.

Can you explain how cross-validation helps in this context?

Cross-validation helps by splitting your data into training and validation sets, ensuring your model generalizes well.

What is regularization, and how does it prevent overfitting?

Regularization adds a penalty for larger coefficients in your model, discouraging complexity and helping to generalize better.

Are there specific metrics I should monitor to detect overfitting?

Yes, you should monitor training vs. validation loss and accuracy; a large gap indicates overfitting.