07cede74ca
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 |
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| .. | ||
| README.txt | ||
| plot_adaboost_hastie_10_2.py | ||
| plot_adaboost_multiclass.py | ||
| plot_adaboost_regression.py | ||
| plot_adaboost_twoclass.py | ||
| plot_bias_variance.py | ||
| plot_ensemble_oob.py | ||
| plot_feature_transformation.py | ||
| plot_forest_importances.py | ||
| plot_forest_importances_faces.py | ||
| plot_forest_iris.py | ||
| plot_gradient_boosting_oob.py | ||
| plot_gradient_boosting_quantile.py | ||
| plot_gradient_boosting_regression.py | ||
| plot_gradient_boosting_regularization.py | ||
| plot_isolation_forest.py | ||
| plot_partial_dependence.py | ||
| plot_random_forest_embedding.py | ||
| plot_random_forest_regression_multioutput.py | ||
| plot_voting_decision_regions.py | ||
| plot_voting_probas.py | ||
README.txt
.. _ensemble_examples: Ensemble methods ---------------- Examples concerning the :mod:`sklearn.ensemble` module.