* Be more specific about logistic regression solver in examples
* Use early stopped SGD (faster) and plot cross-validated error for best models
* Fix LR solver in /plot_voting_probas.pyexamples/ensemble/plot_voting_probas.py
* Fix LR solver & scale data in plot_digits_classification_exercise.py
* Use saga solver in plot_logistic_l1_l2_sparsity.py
* Use LBFGS solver in plot_iris_logistic.py
* Use LBFGS in plot_logistic.py
* Use SAGA solver for Logistic Regression Path example
* Use LBFGS solver in plot_classifier_chain_yeast.py
* Use LBFGS solver in plot_rbm_logistic_classification.py
* typo
* typo
* Bump up pandas dependency to 0.17.1
* Bump up examples minimal deps to match pandas 0.17.1
* Fix figure layout for plot_digits_pipe.py
* Version numbers are not decimal numbers
* Set multinomial, no scaling to keep example simple, fix formatting of example doc
* Missing plt.tight_layout() in plot_voting_probas.py
* Missing plt.tight_layout() in plot_logistic.py
* 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.
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* Example plots render poorly in dev
* flake8 + bias_variance
* title padding
* misc ensemble variance plotting
don't use rcParams to set size of a single figure,
put legend outside of plot
* semisupervised plotting fixes
use explicit kwargs in subplots_adjust, change hspace, don't change aspect ratio of imshow.
* 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
* DOC examples with correct notebook style
* Modifications in examples/ to avoid unwanted notebook style
* Remove last notebook style example
* Space formatting to avoid notebook style
The comment in a few example plots refer to "the mesh [x_min, m_max] x
[y_min, y_max]" when it should be "x_max".
Also fixed the spacing in plot_random_forest_embedding.py `[x_min,
x_max] x [y_min, y_max]` to `[x_min, x_max]x[y_min, y_max]` to comply
with the coding style of other files with similar code (like those in
the current pull request).
Register OneVsRestRegressor as meta estimator
Rename to a more sensible name
Parallel predict and sparse support
Started MultiOutput documentation
Move code to new file multioutput.py
Continuing the move to new multioutput module
Added sample weight support
Better test for sample weights and actually support weights
Added a new test using weighted vs repeated samples to
test sample weight support. Uncovered that weights
were not actually passed on to underlying estimator.
Comment on multiprocess overheads
Move parallel_helper to utils.fixes
This helper works around a python2 limitation on pickling
instance methods
Example of multi-output regression with gradient boosting
Switch to uniform weighted score and updated example
The example now uses a RF with and without the MultiOutput
meta estimator
Added note for removing `score` method
Addressing comments on MultiOutputRegressor
MultiOutputregressor better test for weighted samples
Fix ups
Use explicit keyword argument for passing sample weights and
fix random_state on train-test split in the example
example + benchmark
explanation
make some private functions + fix public API
IForest using BaseForest base class for trees
debug + plot_iforest
classic anomaly detection datasets and benchmark
small modif
BaseBagging inheritance
shuffle dataset before benchmarking
BaseBagging inheritance
remove class label 4 from shuttle dataset
pep8 + rm shuttle.csv bench_IsolationForest.png + doc decision_function
add tests
remove comments
fetching kddcup99 and shuttle datasets
fetching kddcup99 and shuttle datasets
pep8
fetching kddcup99 and shuttle datasets
pep8
new files iforest.py and test_iforest.py
sc
alternative to pandas (but very slow)
in kddcup99.py
faster parser
sc
pep8 + cleanup + simplification
example outlier detection
clean and correct
idem
random_state added
percent10=True in benchmark
mc
remove shuttle + minor changes
sc
undo modif on forest.py and recompile cython on _tree.c
fix travis
cosmit
change bagging to fix travis
Revert "change bagging to fix travis"
This reverts commit 30ea500eb818c7a2c6ea5c3d63e75c6935aa3a35.
add max_samples_ in BaseBagging.fit to fix travis
mc
API : don't add fit param but use a private _fit + update tests + examples to avoid warning
adapt to the new structure of _tree.pyx
cosmit
add performance test for iforest
add _tree.c _utils.c _criterion.c
TST : pass on tests
remove test
relax roc-auc to fix AppVeyor
add test on toy samples
Handle depth averaging at python level
plot example: rm html add png
load_kddcup99 -> fetch_kddcup99 + doc
Take into account arjoly comments
sh -> shuffle
add decision_path code from #5487 to bench
Take into account arjoly comments
Revert "add decision_path code from #5487 to bench"
This reverts commit 46ad44ab487f4fd2728d927cbe09000330e8663e.
fix bug with max_samples != int
In previous commit, I used n_features to set the number of features, and plotted based on that variable. Based on feedback, I removed n_features, and now plotting is based on X.shape[1]. This makes the code general and easy to port. I copied this into an iPython notebook to verify it still outputs the correct graph.
I ported this code into my own, and when I tried plotting, I found the number of features was hard coded to 10. By switching to a set variable, the number of features is no longer a magic number, and the code is more readable.
Rohan Lekhwani
Hanmin Qin