An overview of the hyperparameter optimization process in scikit-learn is [here][1].

Exhaustive grid search will find the optimal set of hyperparameters for a model. The downside is that exhaustive grid search is slow. 

Random search is faster than grid search but has unnecessarily high variance. 

There are also additional strategies in other packages, like [scikit-optimize][2].

What is the most efficient (finding reasonably performant parameters quickly) method for hyperparameter optimization in scikit-learn?

Ideally, I would like working code examples with benchmarks.


  [1]: https://scikit-learn.org/stable/modules/grid_search.html
  [2]: https://scikit-optimize.github.io/