A couple of old model fitting tricks

I picked up many modeling tricks in graduate school working in a computational neuroscience laboratory. We doing machine learning, but we don't know at the time. We called it "automated model fitting." We used custom (and very finicky) algorithms in MATLAB. Now people are blessed with scikit-learn. These ideas might help out for edge cases:

• Have good "fake" data. "Fake" data allows for testing of the algorithms. There is an art to dummy data, it should be noisy but allow the models to converge.
• Linearly transform the data so all dimensions are within the same order of magnitude. Some models have trouble with weighting noise parameters on different scales.