Should the baseline comparison be done on the training or the test set results?
You definitely want the comparison to be based on the test set:

Evaluating on the training set doesn't make sense for all the usual reasons.
Especially in the case of different features, comparing the performance on the training set could be badly misleading: if one of the model overfits, its performance on the training set will appear better but its real performance (on the test set) is likely worse.

Note that it might make sense to study what happens on the training set (e.g. to measure overfitting), but that cannot be the real evaluation used for comparing the models.