DOC Clarify decision trees complexity #32583
Open
+47
−11
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
virchan
approved these changes
Oct 29, 2025
Member
virchan
left a comment
There was a problem hiding this comment.
LGTM! The rendered doc looks good as well! Thanks, @ArturoAmorQ!
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Reference Issues/PRs
Follow-up from #32416.
What does this implement/fix? Explain your changes.
The paragraph about the decision tree complexity was written with the greedy splitter strategy (
splitter='best') in mind. This PR documents howsplitter='random'reduces the complexity by removing the need for sorting the values. It does it by adding a table at the beginning to make the information easier to skim, then jumping into the arguments.Any other comments?
I kept the complexity at a worst-case level, but maybe it's worth mentioning if/how the scikit-learn implementation reduces it, e.g. low-level parallelization.