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ENH add CAP curve#28972

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JosephBARBIERDARNAL wants to merge 102 commits intoscikit-learn:mainscikit-learn/scikit-learn:mainfrom

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Commits

Commits on May 7, 2024

• add cap curve main class and tests

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Commits on May 11, 2024

• linting issue

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• Merge branch 'main' into cap-curve

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Commits on Jun 3, 2024

• Merge branch 'main' into cap-curve

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Commits on Jun 25, 2024

• Merge branch 'main' into cap-curve

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Commits on Sep 29, 2024

• Merge branch 'main' into cap-curve

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Commits on Oct 6, 2024

• Merge branch 'scikit-learn:main' into cap-curve

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• fix cap curve name/description

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• cap curve tests + docstrings + small updates

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Commits on Jan 11, 2025

• Merge branch 'main' into cap-curve

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• various capcurve improvements + tests

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• add CAP curve to display comparisons in doc

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• add CAP curve to display comparisons in doc

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Commits on Feb 5, 2025

• Merge branch 'main' into cap-curve

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Commits on Apr 1, 2025

• Merge branch 'main' into cap-curve

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• Fix matplotlib related test failures and warnings

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• Small improvement to the example

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• Formatting.

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• Consistent ordering of the curves

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• Avoid duplicted plotting of 'chance level'

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• Typo

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• Fix broken doctest

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• Add missing docstring entry for plot_chance_level

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• Add missing See Also and Examples section in the docstrings

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• Illustrate the use of pos_label

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Commits on Apr 5, 2025

• Merge branch 'main' into cap-curve

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• use pos_label for ylabel

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• ensure estimator is fitted and with less than 3 classes

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• ensure estimator is fitted and with less than 3 classes

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• handle pos_label when response_method is decision_function

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• parametrize pos_label test with with_strings

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• unused argument in test

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• better y_pred definition in tests

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• Check that CAP curve is on or above the chance level line

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• fix typo docstring CAP curve

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• Better tests and error handling for estimator input

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• add plot_perfect arg in CAPCurveDisplay

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Commits on Apr 6, 2025

• complete docstring + test for curve position

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• test plot perfect argument style

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• add cap curve to doc/visualization

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Commits on Apr 8, 2025

• complete docstring

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• complete docstring

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• document attributes

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• complete docstring

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• use same sample_weight type as input

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• rename to y_true_bool instead of y_bool

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• comp

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• regressor support

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• better error message

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• use exact match to test error message

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• import re

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• test for response_method="auto"

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• use correct test name

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• plot_perfect for RF in example

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• set perfect line to black and change linestyle

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• test for y_true_cumulative dtype equals cumulative dtype

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• tests for LinearSVC

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Commits on Apr 12, 2025

• extend cap curve tests

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• Merge branch 'main' into cap-curve

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• add to changelog

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• check for valid dtype

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• fix html rendering in doc

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• better test name

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• update LogisticRegression test

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• remove incorrect function mention

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• fix missing definition in test

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• remove dupplicated tests in cap curve and use test_common_display instead

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Commits on Apr 14, 2025

• Merge branch 'main' into cap-curve

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• add test logistic regression

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• update changelog cap curve

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• update example display classif

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• add despine arg

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• docstring for regressors

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Commits on Apr 23, 2025

• Merge branch 'main' into cap-curve

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• add error when y_true has negative values

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• explicit regression option in docstring

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• add pyplot fixture

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• add pyplot fixture

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• add support for regression

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• Merge branch 'main' into cap-curve

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• add support for regression

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• try to fix lorenz curve position

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• try to fix lorenz curve position

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Commits on Apr 25, 2025

• Update narrative of chance level in the example

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Commits on Apr 28, 2025

• Merge branch 'main' into cap-curve

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• update poisson regression non normal example

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• Merge branch 'main' into cap-curve

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• update tweedie regression insurance claim example

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• update poisson regression non normal example

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• remove gini function in examples

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Commits on May 3, 2025

• Merge branch 'main' into cap-curve

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• Update examples/linear_model/plot_poisson_regression_non_normal_loss.py

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• Update examples/linear_model/plot_poisson_regression_non_normal_loss.py

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• Update examples/model_selection/plot_det.py

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• handle explicit classification prob

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• test despine

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• use consistent variable names

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• fix match in tests

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• handle x_max/y_max equal 0

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• fix typo error test

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• add tests for name

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• refacto test attributes and add edge cases

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