* First draft on elasticnet penaly for LogisticRegression
* Some basic tests
* Doc update
* First draft for LogisticRegressionCV.
It seems to be working for binary classification and for multiclass when
multi_class='ovr'. I'm having a hard time figuring out the intricacies
of multi_class='multinomial'.
* Changed default to None for l1_ratio.
added warning message is user sets l1_ratio while penalty is not
elastic-net
* Some more doc
* Updated example to plot elastic net sparsity
* Fixed flake8
* Fixed test by not modifying attribute in fit
* Fixed doc issues
* WIP
* Partially fixed logistic_reg_CV for multinomial.
Also added some comments that are hopefully clear.
Still need to fix refit=False
* Fixed doc issue
* WIP
* Fixed test for refit=False in LogisticRegressionCV
* Fixed Python 2 numpy version issue
* minor doc updates
* Weird doc error...
* Added test to ensure that elastic net is at least as good as L1 or L2
once l1_ratio has been optimized with grid search
Also addressed minor reviews
* Fixed test
* addressed comments
* Added back ignore warning on tests
* Added a functional test
* Scale data in test... Now failing
* elastic-net --> elasticnet
* Updated doc for some attributes and checked their shape in tests
* Added l1_ratio dimension to coefs_paths and scores attr
* improve example + fix test
* FIX incorrect lagged_update in SAGA
* Add non-regression test for SAGA's bug
* FIX flake8 and warning
* Re fixed warning
* Updated some tests
* Addressed comments
* more comments and added dimension to LogisticRegressionCV.n_iter_ attribute
* Updated whatsnew for 0.21
* better doc shape looks
* Fixed whatnew entry after merges
* Added dot
* Addressed comments + standardized optional default param docstrings
* Addessed comments
* use swapaxes instead of unsupported moveaxis (hopefully fixes tests)
* 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
* 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
- No need for `regr.predict(diabetes_X_test)` to run multiple times.
- Use `sklearn.metrics.mean_squared_error`.
- Use `sklearn.metrics.r2_score`, instead of `regr.score`, which
runs `regr.predict` again.
The first argument of np.random.normal is the mean of the distribution, and
not the output shape. The example thus did not add noise but only an intercept
to the model.
* initial commit for return_std
* initial commit for return_std
* adding tests, examples, ARD predict_std
* adding tests, examples, ARD predict_std
* a smidge more documentation
* a smidge more documentation
* Missed a few PEP8 issues
* Changing predict_std to return_std #1
* Changing predict_std to return_std #2
* Changing predict_std to return_std #3
* Changing predict_std to return_std final
* adding better plots via polynomial regression
* trying to fix flake error
* fix to ARD plotting issue
* fixing some flakes
* Two blank lines part 1
* Two blank lines part 2
* More newlines!
* Even more newlines
* adding info to the doc string for the two plot files
* Rephrasing "polynomial" for Bayesian Ridge Regression
* Updating "polynomia" for ARD
* Adding more formal references
* Another asked-for improvement to doc string.
* Fixing flake8 errors
* Cleaning up the tests a smidge.
* A few more flakes
* requested fixes from Andy
* Mini bug fix
* Final pep8 fix
* pep8 fix round 2
* Fix beta_ to alpha_ in the comments
* Small correction of logit->logistic
The function `1 / (1 + np.exp(-x))` is the logistic function.
The logit function is the inverse of the logistic function : `log(x/(1-x))`
* Add axes ticks, legend and colors to the plot
* Fix flake8 errors
* Rename legend labels as per review
* Fix pep8 error
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).
Add gradient calculation in _huber_loss_and_gradient
Add tests to check the correctness of the loss and gradient
Fix for old scipy
Add parameter sigma for robust linear regression
Add gradient formula to robust _huber_loss_and_gradient
Add fit_intercept option and fix tests
Add docs to HuberRegressor and the helper functions
Add example demonstrating ridge_regression vs huber_regression
Add sample_weight implementation
Add scaling invariant huber test
Remove exp and add bounds to fmin_l_bfgs_b
Add sparse data support
Add more tests and refactoring of code
Add narrative docs
review huber regressor
Minor additions to docs and tests
Minor fixes that deals with dealing with NaN values in targets
and old verions of SciPy and NumPy
Add HuberRegressor to robust estimator
Refactored computation of gradient and make docs render properly
Temp
Remove float64 dtype conversion
trivial optimizations and add a note about R
Remove sample_weights special_casing
address @amueller comments
Adrin Jalali
Hanmin Qin