How do I choose the right cross-validation method?

How do I choose the right cross-validation method for my machine learning model?

Choosing the right cross-validation method depends on your dataset size and model type. Are you working with a small or large dataset?

I have a small dataset, around 100 samples.

For small datasets, k-fold cross-validation is often recommended. It helps in maximizing the training data used for each fold. Would you like to know how to implement it?

Yes, please explain how to implement k-fold cross-validation.

To implement k-fold cross-validation, you split your dataset into k subsets. For each subset, you train the model on the remaining k-1 subsets and validate it on the current subset. This process is repeated k times.

What value of k should I use?

A common choice for k is 5 or 10, but you can experiment with different values to see what works best for your model.