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
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
Weird display bug on a Warning box on cross_validation.rst
("Contrary to StratifiedKFold, the ``labels`` of :class:`LeaveOneLabelOut` should not encode the target class to predict...")
Update kneighbors_graph()
Add n_job attribute.
Update Isomap
Add n_job attribute to Isomap.__init__ method.
Conform isomap.py to pep8.
Conform graph.py to pep8.
Update dbscan_.py
Add n_jobs parameter to dbscan function and DBSCAN.__init__ method.
Refactor DBSCAN.__init__ documentation.
Update mean_shift_.py
Add n_jobs parameter to estimate_bandwidth function.
Propagate existing n_jobs parameter in mean_shift to estimate_bandwidth and NearestNeighbors.
Conform mean_shift_.py to pep8.
Conform locally_linear.py to pep8.
Add n_jobs parameter to SpectralClustering.__init__ method.
Add n_jobs parameter to SpectralEmbedding.__init__ method.
Update usage of barycenter_kneighbors_graph function.
Conform spectral_embedding_.py to pep8.
Update label_propagation.py
Conform to pep8.
Refactor imports.
Add n_jobs parameter to LabelSpreading.__init__ method.
Update dbscan function call.
Remove n_jobs argument from dbscan call, as it is already being given by self.get_params().
Add n_jobs parameter to KernelPCA.__init__ method.
Update KernelPCA construction in Isomap.
Remove unnecessary parentesis in isomap.py:211,29.
Update docs: pipeline.rst
Revert "Fix n_jobs usage"
This reverts commit 9480630a69bbb68b01d1176d476222fc665cdd1e.
Fix usage of n_jobs property in Isomap
Sébastien Lerique