What is the difference between cross-validation and holdout methods?

What is the difference between cross-validation and holdout methods?

Cross-validation involves partitioning the data into subsets to train and validate the model multiple times, while holdout uses a single split.

Why would I choose cross-validation over holdout?

Cross-validation provides a more reliable estimate of model performance by using multiple training and validation sets, reducing variance.

Are there any downsides to using cross-validation?

Yes, cross-validation can be computationally expensive and time-consuming, especially with large datasets.

When should I use the holdout method?

The holdout method is suitable for quick evaluations or when computational resources are limited, especially with large datasets.