Enhance ROC Curve Display Tests for Improved Clarity and Maintainability #31266
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Replaced the `data_binary` fixture that filtered classes from a multiclass dataset with a new fixture generating a synthetic binary classification dataset using `make_classification`. This ensures consistent data characteristics, introduces label noise, and better simulates real-world classification challenges.
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@lucyleeow i guess everything is good now |
lucyleeow
approved these changes
Apr 29, 2025
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@lucyleeow, I added 20 features to prevent overfitting. Without them we'd likely get a perfect ROC AUC of 1.0. Despite the added features, training time remains fast, so performance isn't a concern |
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@NEREUScode thanks for explaining. What AUC do you get with 20 features? And what AUC do you get with e.g., 10? |
adrinjalali
approved these changes
Apr 30, 2025
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@lucyleeow 's question stands, but looks good anyway. |
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@lucyleeow I'll run more tests to see if the feature number needs adjusting |
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PR Description:
Summary of Changes:
This PR refactors the
data_binaryfixture in thetest_roc_curve_display.pyfile. The previous fixture filtered a multiclass dataset (Iris) to create a binary classification task. However, this approach resulted in AUC values consistently reaching 1.0, which does not reflect real-world challenges.The new fixture utilizes
make_classificationfromsklearn.datasetsto generate a synthetic binary classification dataset with the following characteristics:flip_y=0.1) to simulate real-world imperfections in the data.class_sep=0.8) set to avoid perfect separation.These changes provide a more complex and representative dataset for testing the
roc_curve_displayfunction and other related metrics, thereby improving the robustness of tests.Reference Issues/PRs:
test_roc_curve_display.py#31243from_cv_resultsinRocCurveDisplay(singleRocCurveDisplay) #30399 (comment)For Reviewers: