* docs: fix broken and redirect links
see #7000
* docs: fix links
see #7000
* docs: merge with master
* docs: fix fnrs and tinyclues logo links
* docs: fix link reference in text
* docs: fix typo
* docs: added back in metaoptimize-qa paragraph
* docs: update language for defunct site
* docs: update stackexchange section
* docs: remove defunct site, move quora to top
* docs: remove defunct link and rearrange links
Depreciation of the GMM class.
Modification of the GaussianMixture class.
Some functions from the original GSoC code have been removed, renamed or simplified.
Some new functions have been introduced (as the 'check_parameters' function).
Some parameters names have been changed :
- covars_ -> covariances_ : to be coherent with sklearn/covariances
Addition of the parameter 'warm_start' allowing to fit data by using the previous computation.
The old examples have been modified to replace the deprecated GMM class by the new GaussianMixture class.
Every exemple use the eigenvectors norm to solve the scale ellipse problem (Issues 6548).
Correction of all commentaries from the PR
- Rename MixtureBase -> BaseMixture
- Remove n_features_
- Fix some problems
- Add some tests
Correction of the bic/aic test.
Fix the test_check_means and test_check_covariances.
Remove all references to the deprecated GMM class.
Remove initialized_.
Add and correct docstring.
Correct the order of random_state.
Fix small typo.
Some fix in prevision of the integration of the new BayesianGaussianMixture class.
Modification in preparation of the integration of the BayesianGaussianMixture class.
Add 'best_n_iter' attribute.
Fix some bugs and tests.
Change the parameter order in the documentation.
Change best_n_iter_ name to n_iter_.
Fix of the warm_start problem.
Fix the divergence error message.
Correction of the random state init in the test file.
Fix the testing problems.
Update and add comments into the monotonic test.
Ensuring consistent transforms for KernelPCA
Taking @vene's changes into account, thanks!
Taking @jakevdp's comment into account
Added more verbose documentation to kernel_pca.py
Specifying that X_fit_ will not be None. @jakevdp
KernelPCA: Fixing more formatting of docstring
Addressed @vene's documentation comments
Addressing that dual_coef_ might not be present in model in docs. @vene
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
Nate George