The link that you posted has many of the techniques that I would suggest, but additionally plotting learning curves can help. This can help you see not just the absolute performance, but can help you get a sense of how far from optimal performance you are.
Learning Curves: If you plot cross-validation (cv) error and training set error rates versus training set size, you can learn a lot. If the two curves approach each other with low error rate, then you are doing well.
If it looks like the curves are starting to approach each other and both heading/staying low, then you need more data.
If the cv curve remains high, but the training set curve remains low, then you have a high-variance situation. You can either get more data, or use regularization to improve generalization.
If the cv stays high and the training set curve comes up to meet it, then you have high bias. In this case, you want to add detail to your model.