What are the best practices for validation size in data processing?

What are the best practices for validation size in data processing?

The best practices for validation size include ensuring a representative sample, balancing classes, and considering the total dataset size.

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

A common approach is to use 10-20% of your total dataset for validation, but it can vary based on your specific use case.

What if my dataset is very small?

If your dataset is small, you might consider using techniques like cross-validation to make the most of your data.

Are there any tools that can help with this?

Yes, tools like Scikit-learn and TensorFlow offer built-in functions for splitting datasets and managing validation sizes.