* Fix#4630, #4421: Preserve correct ordering between metrics, and always use last metric for early stopping
* Clarify semantics of early stopping in presence of multiple valid sets and metrics
* Add a test
* Fix lint
* Brought the silent parameter for the SKLearn-like API back, marked it deprecated.
- added deprecation notice and warning
- removed silent from the tests for the SKLearn-like API
* Added SKLearn-like random forest Python API.
- added XGBRFClassifier and XGBRFRegressor classes to SKL-like xgboost API
- also added n_gpus and gpu_id parameters to SKL classes
- added documentation describing how to use xgboost for random forests,
as well as existing caveats
* Enable xgb_model parameter in XGClassifier scikit-learn API
https://github.com/dmlc/xgboost/issues/3049
* add test_XGBClassifier_resume():
test for xgb_model parameter in XGBClassifier API.
* Update test_with_sklearn.py
* Fix lint
* Unify logging facilities.
* Enhance `ConsoleLogger` to handle different verbosity.
* Override macros from `dmlc`.
* Don't use specialized gamma when building with GPU.
* Remove verbosity cache in monitor.
* Test monitor.
* Deprecate `silent`.
* Fix doc and messages.
* Fix python test.
* Fix silent tests.
* update description of early stopping rounds
the description of early stopping round was quite inconsistent in the scikit-learn api section since the fit paragraph tells that when early stopping rounds occurs, the last iteration is returned not the best one, but the predict paragraph tells that when the predict is called without ntree_limit specified, then ntree_limit is equals to best_ntree_limit.
Thus, when reading the fit part, one could think that it is needed to specify what is the best iter when calling the predict, but when reading the predict part, then the best iter is given by default, it is the last iter that you have to specify if needed.
* Update sklearn.py
* Update sklearn.py
fix doc according to the python_lightweight_test error
* use gain for sklearn feature_importances_
`gain` is a better feature importance criteria than the currently used `weight`
* added importance_type to class
* fixed test
* white space
* fix variable name
* fix deprecation warning
* fix exp array
* white spaces
* Fix#3747: Add coef_ and intercept_ as properties of sklearn wrapper
Scikit-learn expects linear learners to expose `coef_` and `intercept_`
as properties.
Closes#3747.
* Fix lint
The `save_model()` and `load_model()` method only saves the part of the model
that's common to all language interfaces and do not preserve Python-specific
attributes, such as `feature_names`. More crucially, label encoder is not
preserved either; this is needed for the scikit-learn wrapper, since you may
have string labels.
Fix: Explicitly recommend pickling as the way to save scikit-learn model
objects.
* Add scikit-learn tests
Goal is to pass scikit-learn's check_estimator() for XGBClassifier,
XGBRegressor, and XGBRanker. It is actually not possible to do so
entirely, since check_estimator() assumes that NaN is disallowed,
but XGBoost allows for NaN as missing values. However, it is always
good ideas to add some checks inspired by check_estimator().
* Fix lint
* Fix lint
* Fix#3648: XGBClassifier.predict() should return margin scores when output_margin=True
* Fix tests to reflect correct implementation of XGBClassifier.predict(output_margin=True)
* Fix flaky test test_with_sklearn.test_sklearn_api_gblinear
* Add XGBRanker to Python API doc
* Show inherited members of XGBRegressor in API doc, since XGBRegressor uses default methods from XGBModel
* Add table of contents to Python API doc
* Skip JVM doc download if not available
* Show inherited members for XGBRegressor and XGBRanker
* Expose XGBRanker to Python XGBoost module directory
* Add docstring to XGBRegressor.predict() and XGBRanker.predict()
* Fix rendering errors in Python docstrings
* Fix lint
* added xgbranker
* fixed predict method and ranking test
* reformatted code in accordance with pep8
* fixed lint error
* fixed docstring and added checks on objective
* added ranking demo for python
* fixed suffix in rank.py
* Revert "Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)"
This reverts commit 44811f233071c5805d70c287abd22b155b732727.
* Document behavior of predict() for DART booster
* Add notice to parameter.rst
* Add option to use weights when evaluating metrics in validation sets
* Add test for validation-set weights functionality
* simplify case with no weights for test sets
* fix lint issues
* Added kwargs support for Sklearn API
* Updated NEWS and CONTRIBUTORS
* Fixed CONTRIBUTORS.md
* Added clarification of **kwargs and test for proper usage
* Fixed lint error
* Fixed more lint errors and clf assigned but never used
* Fixed more lint errors
* Fixed more lint errors
* Fixed issue with changes from different branch bleeding over
* Fixed issue with changes from other branch bleeding over
* Added note that kwargs may not be compatible with Sklearn
* Fixed linting on kwargs note
* Added n_jobs and random_state to keep up to date with sklearn API.
Deprecated nthread and seed. Added tests for new params and
deprecations.
* Fixed docstring to reflect updates to n_jobs and random_state.
* Fixed whitespace issues and removed nose import.
* Added deprecation note for nthread and seed in docstring.
* Attempted fix of deprecation tests.
* Second attempted fix to tests.
* Set n_jobs to 1.
* Add option to choose booster in scikit intreface (gbtree by default)
* Add option to choose booster in scikit intreface: complete docstring.
* Fix XGBClassifier to work with booster option
* Added test case for gblinear booster
