Hyperparameters have an impact on the models’ performance and should be wisely chosen;
The search for the best hyperparameters can be automated with a grid-search approach or a randomized search approach;
A grid-search is expensive and does not scale when the number of hyperparameters to optimize increase. Besides, the combination are sampled only on a regular grid.
A randomized-search allows a search with a fixed budget even with an increasing number of hyperparameters. Besides, the combination are sampled on a non-regular grid.
To go further#
You can refer to the following scikit-learn examples which are related to the concepts approached during this module: