Co-authored-by: rasbt <mail@sebastianraschka.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
* added code from PR #14627
* fixed error handling None as n_features_to_select
* added test for error message and percentage passing
* linting
* more linting
* even more linting
* sklearn/feature_selection/_rfe.py make int casting simpler
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* exchange redundant test case with small float
* add what's new
* removed useless import
* lint
* Fix whats_new/v0.24.rst
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* move whats new section
* disambiguate choice between 1 feature and 100% of features
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* move checking for negatives to _fit
* update error to include nones
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* markdown style in what's new
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* Update sklearn/feature_selection/tests/test_rfe.py
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* forgot to delete line
* fix test for negative n_features error
* BUG Fixes issues
* added case for float >1 and more detailed error messages
* lint
* more lint
* more lint
* CLN Minor adjustments
* BLD Force build on ci
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* discrete_features str and value check
* Update if logic
* Add discrete_features bad str value test
* Remove unnecessary nested isinstance str check
* Add back nested isinstance str check
* New/updates to tests
* Add v0.21 whats new entry
* Undo v0.21 whats new entry
* Added ChangedBehaviorWarning in sgd models
if tol is None while max_iter is set
* Changed to FutureWarning and clarified None meaning
* Ignored warningin tests
* Ignore warnings in tests, round 2
* Change default solver in LogisticRegression
* This is an API change, not a feature
* Decrease numerical precision in LogisticRegression doctest
* ENH add multi_class='auto' for LR, default from 0.22
* No warning when binary
* add FutureWarning for methods with defaults=3
* add explicit cv values to fix assertion errors
* add tests for catching the FutureWarning
* Write current deprecation version
* Add deprecation in docstring
* change default cv value to None
* change cv from 3 to 5 in the examples
* upgrade doctests
* update doctest in tutorial
* update doctest in cross-validation doc
* fix tests
* add entry to whats new
* address Gael comments
* address Gael comments 2
* fix wrong indentation
* update doc
* add docstring deprecation warning in CV subclasses
* address Andy's comments
* fix PR number
* fix flake8
* add filterwarnings in tests
* fix doctests
* cv=None mendatory in Ridge
* fix warning related errors
* skip some doctests warnings
* make travis happy
* change from deprecated to versionchanged
* fix doctests and remove skipping
* address comments
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#### Reference Issues/PRs
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Fixes#11128.
#### What does this implement/fix? Explain your changes.
Issues deprecation warning message for the default n_estimators parameter for the forest classifiers. Test added for the warning message when the default parameter is used.
#### Any other comments?
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#### Reference Issue
Continuation of work from [PR #6717](https://github.com/scikit-learn/scikit-learn/pull/6717).
#### What does this implement/fix? Explain your changes.
Will merge in master (this branch is a year old) and make changes as discussed in previous PR discussion to make it ready for merging in.
* Fix edge case of tied CV scores in RFECV
In the feature_selection module, RFECV selects the model with the
highest cross-validation score. In the event of CV score ties, one
expects RFECV to return the best model with the fewest features.
This fix addresses such an edge case where two or more models have
identical cross-validation scores.
* Adding an entry to what's new addressing bug fix in RFECV edge case
* Re-add what's new entry
* Use double backticks in whats_new entry
* some bug fixes.
* minor fixes to whatsnew
* typo in whatsnew
* add test for n_components = 1 transform in dict learning
* feature extraction doc fix
* fix broken test
* revert aggressive input validation changes
* in SelectFromModel, don't store threshold_ in transform. If we called "fit", use estimates from last "fit".
* move score from EllipticEnvelope to OutlierDetectionMixin
* revert changes to Tfidf documentation
* remove dummy input validation from whatsnew
* fix text feature tests
* rewrite from_model threshold again...
* remove stray condition
* fix self.estimator -> estimator, slightly more interesting test
* typo in comment
* Fix issues in SparseEncoder, add tests.
more explicit explanation of SparseEncoder change, add issue numbers to whatsnew
* minor fixes in whats_new.rst
* slightly more consistency with tuples for shapes
* not longer typo
Nicolas Hug
Hanmin Qin