GridSearchCV vs RandomSearchCV
Can somebody explain in-detailed differences between GridSearchCV and RandomSearchCV? And how the algorithms work under the hood?
As per my understanding from the documentation:
This uses a random set of hyperparameters. Useful when there are many hyperparameters, so the search space is large. It can be used if you have a prior belief on what the hyperparameters should be.
Creates a grid over the search space and evaluates the model for all of the possible hyperparameters in the space. Good in the sense that it is simple and exhaustive. On the minus side, it may be prohibitively expensive in computation time if the search space is large (e.g. very many hyper parameters).