What are the best validation techniques for small datasets?

What are the best validation techniques for small datasets?

For small datasets, techniques like k-fold cross-validation, leave-one-out cross-validation, and bootstrapping are effective.

Can you explain k-fold cross-validation?

Sure! K-fold cross-validation involves splitting the dataset into k subsets. The model is trained on k-1 subsets and tested on the remaining one, repeating this process k times.

What about leave-one-out cross-validation?

Leave-one-out cross-validation is a special case of k-fold where k equals the number of data points. Each time, one data point is used for testing, and the rest for training.

Are there any drawbacks to these techniques?

Yes, they can be computationally expensive, especially with larger datasets. For very small datasets, they might also lead to overfitting.