Hello,
I don’t have a very large dataset, and I want to use 10-fold cross-validation to see how well my model performs on unseen data.
I see a lot of code using cross-validation to establish the best hyper-parameters (#iterations, learning rate, etc). Then when that’s been done, we run the model on all the data with those optimal hyper-parameters.
Does that now give me an idea of how well the model performs on unseen data, or do I need to embed that whole story into another level of cross-validation?
Many thanks.