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ENH use cv_results in the different curve display to add confidence intervals #21211

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6b09fe6
ENH add return_indices in cross_validate
glemaitre Oct 1, 2021
f5b6efb
iter
glemaitre Oct 1, 2021
e6d86f5
ENH add from_cv_results into CalibrationDisplay
glemaitre Oct 1, 2021
bae6a9b
changelog
glemaitre Oct 1, 2021
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12 changes: 6 additions & 6 deletions doc/modules/cross_validation.rst
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Expand Up @@ -208,8 +208,8 @@ two ways:
- It allows specifying multiple metrics for evaluation.

- It returns a dict containing fit-times, score-times
(and optionally training scores as well as fitted estimators) in
addition to the test score.
(and optionally training scores, fitted estimators, train-test split indices)
in addition to the test score.

For single metric evaluation, where the scoring parameter is a string,
callable or None, the keys will be - ``['test_score', 'fit_time', 'score_time']``
Expand All @@ -220,10 +220,10 @@ following keys -

``return_train_score`` is set to ``False`` by default to save computation time.
To evaluate the scores on the training set as well you need to set it to
``True``.

You may also retain the estimator fitted on each training set by setting
``return_estimator=True``.
``True``. You may also retain the estimator fitted on each training set by
setting ``return_estimator=True``. On the same line of thought, you may set
`return_indices=True` to retain the training and testing indices used to split
the dataset into train and test sets for each cv split.

The multiple metrics can be specified either as a list, tuple or set of
predefined scorer names::
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16 changes: 16 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -38,13 +38,29 @@ Changelog
:pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
where 123456 is the *pull request* number, not the issue number.

:mod:`sklearn.calibration`
..........................

- |Feature| :class:`calibration.CalibrationDisplay` has a new method
:meth:`~calibration.CalibrationDisplay.from_cv_results` allowing to plot
calibration curves together with standard deviation uncertainty using
cross-validation results.
:pr:`21211` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.linear_model`
...........................

- |Fix| :class:`linear_model.LogisticRegression` now raises a better error
message when the solver does not support sparse matrices with int64 indices.
:pr:`21093` by `Tom Dupre la Tour`_.

:mod:`sklearn.model_selection`
..............................

- |Enhancement| :func:`model_selection.cross_validate` accepts a new parameter
`return_indices` to return the train-test indices of each cv split.
:pr:`21211` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.utils`
....................

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