I am trying to plot learning curves to determine if my model is undercutting or overfitting, but cannot get learning curves like or Not sure what I did is wrong. I have several stupid simple questions:
(1) Does that mean, for each chosen number of training dataset, we train a model? Suppose m (total training data size) = 100000, n (test data size) = 20000. We build model for training dataset size = 0.1m, 0.2m, 0.3m,.... until m, and then for each model, we evaluate training errors using training dataset with 0.1m, 0.2m,... and test errors (always using n=2000 test dataset)?
(2) What does the error mean here? For regression problems, is it the total errors or mean errors?
(3) For classification model, I saw many people use accuracy as error to make plot. Can we use the cross entropy instead?