c1f58745be
* 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) |
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|---|---|---|
| .. | ||
| README.txt | ||
| plot_ard.py | ||
| plot_bayesian_ridge.py | ||
| plot_huber_vs_ridge.py | ||
| plot_iris_logistic.py | ||
| plot_lasso_and_elasticnet.py | ||
| plot_lasso_coordinate_descent_path.py | ||
| plot_lasso_dense_vs_sparse_data.py | ||
| plot_lasso_lars.py | ||
| plot_lasso_model_selection.py | ||
| plot_logistic.py | ||
| plot_logistic_l1_l2_sparsity.py | ||
| plot_logistic_multinomial.py | ||
| plot_logistic_path.py | ||
| plot_multi_task_lasso_support.py | ||
| plot_ols.py | ||
| plot_ols_3d.py | ||
| plot_ols_ridge_variance.py | ||
| plot_omp.py | ||
| plot_polynomial_interpolation.py | ||
| plot_ransac.py | ||
| plot_ridge_coeffs.py | ||
| plot_ridge_path.py | ||
| plot_robust_fit.py | ||
| plot_sgd_comparison.py | ||
| plot_sgd_early_stopping.py | ||
| plot_sgd_iris.py | ||
| plot_sgd_loss_functions.py | ||
| plot_sgd_penalties.py | ||
| plot_sgd_separating_hyperplane.py | ||
| plot_sgd_weighted_samples.py | ||
| plot_sparse_logistic_regression_20newsgroups.py | ||
| plot_sparse_logistic_regression_mnist.py | ||
| plot_theilsen.py | ||
README.txt
.. _linear_examples: Generalized Linear Models ------------------------- Examples concerning the :mod:`sklearn.linear_model` module.