Bad results for training set and good results for test set
Seems quite strange. I bet you've got a bug or test set size is small enough. Like if there're only 2 objects: 1 positive and 1 negative, you could have classified them correctly even with a random classifier. I assume your test set is not that small, which means there's a bug somewhere. Also, in theory, if both of your sets are large enough and are drawn from the same data distribution, you can usually have one of 2 types of behavior:

about the same result both on training and testing sets (when your model underfits or fits well)
better results on the training set then on the testing (when your model if overfit)