What is the ideal size for a validation set in machine learning?

What is the ideal size for a validation set in machine learning?

The ideal size for a validation set typically ranges from 10% to 20% of your total dataset, depending on its size.

Why is it important to have a validation set?

A validation set helps to tune model parameters and prevent overfitting by providing a separate dataset to evaluate performance.

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

Yes, cross-validation is a great alternative as it uses multiple subsets of the data to validate the model, providing a more robust evaluation.

What happens if my validation set is too small?

If your validation set is too small, it may not accurately represent the overall dataset, leading to unreliable performance metrics.