What is the impact of validation size on model performance?

What is the impact of validation size on model performance?

The validation size can significantly affect model performance. A larger validation set provides a better estimate of how the model will perform on unseen data, while a smaller set may lead to overfitting.

How do I determine the right validation size for my dataset?

A common approach is to use 10-20% of your dataset for validation. However, it can vary based on the total size of your dataset and the complexity of your model.

What happens if my validation set is too small?

If your validation set is too small, it may not represent the overall data distribution well, leading to unreliable performance metrics and potentially misleading conclusions about your model's effectiveness.

Can I use cross-validation instead of a separate validation set?

Yes, cross-validation is a great alternative. It allows you to use your entire dataset for both training and validation, providing a more robust evaluation of your model's performance.