ENH add from_cv_results in RocCurveDisplay (Multi-display)#30359
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lucyleeow wants to merge 3 commits intoscikit-learn:mainfrom
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ENH add from_cv_results in RocCurveDisplay (Multi-display)#30359lucyleeow wants to merge 3 commits intoscikit-learn:mainfrom
from_cv_results in RocCurveDisplay (Multi-display)#30359lucyleeow wants to merge 3 commits intoscikit-learn:mainfrom
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lucyleeow
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Nov 28, 2024
| zip(cv_results["estimator"], cv_results["indices"]["test"], fold_name_) | ||
| ): | ||
| y_true = _safe_indexing(y, test_indices) | ||
| y_pred = _get_response_values_binary( |
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I could have used _validate_and_get_response_values instead here. I decided against because it does other checking that we don't need here but 🤷
This was referenced Nov 29, 2024
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closing in favour of #30399 |
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Reference Issues/PRs
Supercedes #25939
This is part of a group of draft PRs to determine best API for adding plots for cv results to our displays.
from_cv_resultsinRocCurveDisplay(singleRocCurveDisplay) #30399)For all 3 options we take the output of
cross_validate, and use the fitted estimator and test indicies. No fitting is done in the display.We do recalculate the predictions (which would have already been done in
cross_validate), which could be avoided if we decided to changecross_validateto optionally return the predictions as well (note this would makecross_val_predictredundant).See more thread: #25939 (comment)). I think should be outside of the scope of this body of work though.
What does this implement/fix? Explain your changes.
Essentially updates #25939 to main and removes the average and variation parts.
With this approach we have to create a new
Multi...Displayclass for each display. These could be useful to users who want to plot curves that don't come cv but if they are plotting from cv we discourage them to use this class directly (and usefrom_cv_resultsinstead)Any other comments?
Plot looks like:
