Hyper-parameters are parameters which are not estimated as an integral part of the model. We decide on those parameters but we don’t estimate them within, but rather beforehand. Therefore they are called hyper-parameters, as in “above” sense.
Almost all machine learning algorithms have some hyper-parameters. Data-driven choice of hyper-parameters means typically, that you re-estimate the model and check performance for different hyper-parameters’ configurations. This adds considerable computational burden. One popular approach to set hyper-parameters is based on a grid-search over possible values using the validation set. Faster and simpler ways to intelligently choose hyper-parameters’ values would go a long way in keeping the stretched computational cost at a level you can tolerate.
Enter the paper “Random Search for Hyper-Parameter Optimization” by James Bergstra and Yoshua Bengio, suggesting with a straight face not to use grid-search but instead, look for good values completely at random. This is very counterintuitive, for how can a random guesses within some region compete with systematically covering the same region? What’s the story there?
Below I share the message of that paper, along with what I personally believe is actually going on (and the two are very different).