#### Reference Issues/PRs
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This PR addresses issue #12466.
#### What does this implement/fix? Explain your changes.
This PR does the 3 following things:
- Rewrite the `cv` parameter description in `GridSearchCV`
- Link the new `CV splitter` description to an existing example
- Add an example with a custom iterable
Thanks for reviewing this!
Close#12466
* 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
* Created 'cross-validation estimator' in glossary and referenced it where
needed
* Addressed adrinjalali comments
* updated glossary entry
* updaed docstrings according to commets
* 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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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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In order to fix#11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
#### 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
* rm dupes
* add check_supervised_y_no_nan in classifier checks: this implies changes for Ridge classifiers
* fix docstrings/comments
* FIX check fitting 1d X array raises error and FIX check fitting 2d array with only 1 feature either works or returns informative message
* modify check_fit2d_1sample in common tests so that it checks fitting either works or returns an informative message
* rm SpectralClustering case for the moment
* uniformize error messages for 1 sample case and fix SpectralClustering with ensure_min_samples=2
* add unit test for mean_shift when n_samples * quantile < 1
* FIX travis with ensure_min_samples=2 in _PLS
* try fix for failing tSNE test
* typos
* take @agramfort's review into account
* sc to fix string in gaussian_process
* add the class that is present to preserve information of previous message in gpc.py
* 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
Rüdiger Busche
Hanmin Qin