Retraining after Cross Validation with libsvm

Question

I know that Cross validation is used for selecting good parameters. After finding them, i need to re-train the whole data without the -v option.

But the problem i fa

Answer 1

If you use your entire dataset to determine your parameters, then train on that dataset, you are going to overfit your data. Ideally, you would divide the dataset, do the parameter search on a portion (with CV), then use the other portion to train and test with CV. Will you get better results if you use the whole dataset for both? Of course, but your model is likely to not generalize well. If you want determine true performance of your model, you need to do parameter selection separately.