Yes, I think that was better, because the ground truth is Kepler's third law and jimrandomh pointed out your method actually recaptures a (badly obfuscated and possibly overfit) variant of it.
"Dimensionality" is totally relevant in any approach to supervised learning. But it matters even without considering the bias/variance trade-off, etc.
Imagine that you have an high-dimensional predictor, of which one dimension completely determines the outcome and the rest are noise. Your shortest possible generating algorithm is going to have to pick out the relevant dimension. So as the dimensionality of the predictor increases, the algorithm length will necessarily increase, just for information-theoretic reasons.
recaptures a (badly obfuscated and possibly overfit) variant of it.
How do you overfit Kepler's law?
edit: Retracted. I see now looking at the actual link the result wasn't just obfuscated but wrong, and so the manner in which it's wrong can overfit of course (and that matches the results).
This is the bimonthly 'What are you working On?' thread. Previous threads are here. So here's the question:
What are you working on?
Here are some guidelines: