DOC add links to plot_model_complexity_influence example in SVM and c… #31684
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
…omputational performance docs
There was a problem hiding this comment.
thanks for you PR. We have however already decided to not add any more references to this example in #30814. It was also marked as such in the issue description.
(I have still checked the rest of your contribution: And an addition in doc/modules/svm.rst is not useful I think, the place is too genereral.)
I will therefore close this PR. Happy to review another PR from you later.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
This PR adds two new documentation/example files to improve understanding of SVM computational performance:
Model complexity influence example file: Demonstrates how different SVM parameters affect model complexity and performance trade-offs.
Computational performance documentation: Provides guidance on SVM performance optimization, parameter tuning for speed, and computational considerations.
These additions help users better understand SVM performance characteristics and make informed decisions about parameter selection for their specific use cases.