What is the difference between LOOCV and Holdout?

Can you explain the difference between LOOCV and Holdout in machine learning?

Sure! LOOCV stands for Leave-One-Out Cross-Validation, where each data point is used once as a test set while the rest form the training set. Holdout, on the other hand, involves splitting the dataset into two parts: a training set and a test set.

What are the advantages of using LOOCV over Holdout?

LOOCV provides a more accurate estimate of model performance since it uses almost all data for training. However, it can be computationally expensive. Holdout is simpler and faster but may lead to higher variance in performance estimates.

Are there any situations where Holdout is preferred?

Yes, Holdout is often preferred when dealing with large datasets, as it saves time and computational resources. It's also useful when you want a quick assessment of model performance.

Can you give an example of when to use each method?

Certainly! Use LOOCV for small datasets where every data point is valuable. Use Holdout for large datasets where speed is essential and a quick evaluation is sufficient.