Skip to content
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 6 additions & 6 deletions jupyter-book/tuning/parameter_tuning_module_take_away.md
Original file line number Diff line number Diff line change
Expand Up @@ -8,12 +8,12 @@
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.
- A grid-search can be computationally expensive and becomes less attractive as
the number of hyperparameters to explore increases. Moreover, the combinations
are sampled on a fixed, regular grid.
- A randomized-search allows exploring within a fixed budget, even as the number
of hyperparameters increases. In this case, combinations can be sampled either
on a regular grid or from a given distribution.

## To go further

Expand Down