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

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 and scikit-hyperband.

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.

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

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, including scikit-optimize, auto-sklearn, and scikit-hyperband.

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.

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

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.

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.

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

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 and scikit-hyperband.

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.