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DOC Rework plot_adjusted_for_chance_measures.py example #23708

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Merged
merged 27 commits into from
Aug 30, 2022

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ArturoAmorQ
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@ArturoAmorQ ArturoAmorQ commented Jun 21, 2022

Reference Issues/PRs

Related to #23528.

What does this implement/fix? Explain your changes.

Part of the "Reworking examples" series. As mentioned in this comment, the plot_adjusted_for_chance_measures.py example can benefit from a "tutorialization".

Any other comments?

Side effects:

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@ogrisel ogrisel left a comment

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Another batch of comments. Other than that, LGTM! Thanks very much @ArturoAmorQ !

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More feedback (probably the last pass ;):

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Please add yourself to the list of authors of this example. Once all the remaining %-interpolated strings are converted to f-strings, LGTM.

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ogrisel commented Aug 16, 2022

Thanks for making the plots color-blind friendlier.

I think we could make those plots even more easy to read by dropping homogeneity and completeness.

I think doing the analysis for V-measure is enough and homogeneity and completeness do not bring more information to the narrative.

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Thanks for your valuable time, @ogrisel! All your comments have been addressed.

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ogrisel commented Aug 26, 2022

Thanks for your valuable time, @ogrisel! All your comments have been addressed.

The plots are much more readable now. Thanks for your effort and thanks @cmarmo for thinking about making our example plots friendlier to a wider audience.

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Thank you for the PR!

ArturoAmorQ and others added 2 commits August 28, 2022 13:49
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A few more small suggestions, otherwise LGTM

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Thanks for your time and comments, @thomasjpfan

@thomasjpfan thomasjpfan merged commit 474289b into scikit-learn:main Aug 30, 2022
@ArturoAmorQ ArturoAmorQ deleted the chance_in_clustering branch September 2, 2022 08:41
glemaitre pushed a commit to glemaitre/scikit-learn that referenced this pull request Sep 12, 2022
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3 participants