I have a training data set for a binary classification problem. There exist two possible scenarios, one is that all of the training data set are labeled as positive; another one is that the training data set includes labeled positive ones and labeled negative ones.
Assume that I use this training data set to train a decision tree. How do these two different scenarios affect the trained tree?
Moreover, if the ratio of positive and negative ones changes, how does this change affect the built tree model?