When should I use a holdout set in machine learning?

When should I use a holdout set in machine learning?

A holdout set is used to evaluate the performance of a model after training. It's essential when you want to avoid overfitting.

How do I split my data into training and holdout sets?

You can split your data randomly, typically using 70-80% for training and 20-30% for the holdout set.

Is there a specific size for the holdout set?

The size of the holdout set can vary, but it should be large enough to provide a reliable estimate of model performance.

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

Yes, cross-validation is an alternative that allows you to use all your data for training and testing, but it can be more computationally intensive.