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
ENH NonBLASDotWarning -> EfficiencyWarning; Improve error message
DOC Add exceptions module to modules/classes.rst
MAINT Move ConvergenceWarning, UndefinedMetricWarning et al into exceptions
MAINT Remove ChangedBehaviorWarning from base
DOC/FIX Improve DataConversionWarning's docstring
- 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
- Typo fixed in linear_model documenation
- Usage of matplotlib.pyplot instead of matplotlib.pylab
- Removed trailing backslash in import statements
- Renamed _modweiszfeld_step to _modified_weiszfeld_step
- Renamed variable fst to first_elem in _lstq
- Made get_n_jobs private in utils/__init__.py
Before this change, output is (http://scikit-learn.org/dev/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#example-linear-model-plot-logistic-l1-l2-sparsity-py):
C=10
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=100
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1000
Sparsity with L1 penalty: 4.69%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
With this change, output is:
C=100.00
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9110
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1.00
Sparsity with L1 penalty: 9.38%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=0.01
Sparsity with L1 penalty: 85.94%
score with L1 penalty: 0.8625
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.8915
Shota