When should I use the holdout method in machine learning?

When should I use the holdout method in machine learning?

The holdout method is used to evaluate a model's performance by splitting the dataset into training and testing sets. It's ideal when you have a large dataset.

What are the advantages of using the holdout method?

The main advantages include simplicity, speed, and the ability to quickly assess model performance without complex cross-validation.

Are there any downsides to using the holdout method?

Yes, it can lead to high variance in performance estimates, especially if the dataset is small or not representative.

When is it better to use cross-validation instead?

Cross-validation is better when you have a small dataset, as it provides a more reliable estimate of model performance by using multiple splits.