What is holdout validation?

Can you explain what holdout validation is?

Holdout validation is a technique used to assess the performance of a machine learning model by splitting the dataset into two parts: a training set and a test set.

How do you typically split the data for holdout validation?

Typically, the data is split into 70-80% for training and 20-30% for testing, but the exact ratio can vary based on the dataset size.

What are the advantages of using holdout validation?

The main advantages are its simplicity and speed, as it requires less computational resources compared to other methods like cross-validation.

Are there any downsides to holdout validation?

Yes, one downside is that it can lead to high variance in performance estimates, especially if the dataset is small.