* 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
topisan
Hanmin Qin