Perhaps a thing with lots of parameters and some tools/rules-of-thumb for debugging might be called a "gray box" where a "real" statical model with a defined distribution, tests of hypothesis validity and so-forth could be called a "white box".
Right. Being able to trouble shoot models with some level of certainty is better than nothing. Tuning neural nets is still more of an art than a science in some respects, I'm not aiming for anything magical here. My first step with neural nets is to make them usable.
Perhaps a thing with lots of parameters and some tools/rules-of-thumb for debugging might be called a "gray box" where a "real" statical model with a defined distribution, tests of hypothesis validity and so-forth could be called a "white box".