* 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
* Fix Rouseeuw1984 broken link
* Change label vbgmm to bgmm
Previously modified with PR #6651
* Change tag name
Old refers to new tag added with PR #7388
* Remove prefix underscore to match tag
* Realign to fit 80 chars
* Link to metrics.rst.
pairwise metrics yet to be documented
* Remove tag as LSHForest is deprecated
* Remove all references to randomized_l1 and sphx_glr_auto_examples_linear_model_plot_sparse_recovery.py.
It is deprecated.
* Fix few Sphinx warnings
* Realign to 80 chars
* Changes based on PR review
* Remove unused ref in calibration
* Fix link ref in covariance.rst
* Fix linking issues
* Differentiate Rouseeuw1999 tag within file.
* Change all duplicate Rouseeuw1999 tags
* Remove numbers from tag Rousseeuw
This example trains several tree based ensemble methods and uses
them to transform the data into a high dimensional, sparse space.
The trains a linear model on this new feature space. The idea is
taken from:
Practical Lessons from Predicting Clicks on Ads at Facebook Junfeng Pan,
He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine
Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
International Workshop on Data Mining for Online Advertising (ADKDD)
https://www.facebook.com/publications/329190253909587/
@amueller provided feedback on improving my original PR (#4665) of the
plot_ensemble_oob.py script.
A number of major changes were made accordingly:
- Used `matplotlib.pyplot` instead of `pylab`.
- To improve the run-time to <10secs, I reduced the dimensionality of
the sample dataset and set the max. number of estimators to 150.
- To avoid OOB warnings, the min. number of estimators was set to 15.
Values <15 would still raise the warnings.
- The script is PEP8-compliant via the `pep8` command-line script. I
needed to move `print(__doc__)` and author list comments.
- Re-added @amueller to the author list (had mistakenly been removed).
- Added a link to this example to the user-guide under the `Ensemble
Methods` section.
Thomas Fan