Part of #11992.
These were all the things that seemed pretty straight-forward. It's actually a bit bulky but should still be easy to review, hopefully.
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
- Renamed TheilSen to TheilSenRegressor
- Renamed n_iter parameter to max_iter
- Better warning message when maximum iteration number reached
- Added docstring for fit
- Removed backend and max_nbytes parameters in Parallel
- Removed n_dim return value from _check_subparams
- Some PEP8 corrections
- Added an attribute n_iter_ to show number of iterations
- Removed unnecessary array to list conversion ix = list(ix)
- Remove random_state_ as attribute
Andreas Mueller