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* add parameter

* implement

* fix bug

* fix bug

* fix according comment

* add test

* Update test_engine.py

* Update test_engine.py

* Update test_engine.py

* [mmlspark] Fix cached predictor causing bad values for predicted probabilities

* updated based on comments

* removed tabs

* change network retry delay strategy

* refine

* norm the lambda scores

* change default to false

* update doc

* typo

* Update Parameters.rst

* Update config.h

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update rank_objective.hpp

* Update Parameters.rst

* Update config.h

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* fix the bug in bin with small values

* Update bin.cpp

* Update test_engine.py

* Lambdas and hessians need to factor sigmoid_ into the computation. Additionally, the sigmoid function has an arbitrary factor of 2 in the exponent; it is not just non-standard but the gradients are not computed correctly anyway.

* Update unit test

* Also remove a heuristic that normalizes the gradient by the difference in scores.

* Also fix unit test after removing the heuristic

* Fix bug where small values of max_bin cause crash.

* Revert "Fix bug where small values of max_bin cause crash."

This reverts commit fe5c8e2547057c1fa5750bcddd359dd7708fab4b.

* Fix bug where small values of max_bin cause crash.

* Reset random seed in test, remove extra blank line.

* Minor bug fix. Remove extra blank line.

* Change old test to account for new binning behavior.

* fix nan in tree model

* fix

* fix metric alias

* fix format

* updated docs

* simplify alias in objective function

* move the alias parsing to config.cpp

* updated docs

* fix multi-class aliases

* updated regression aliases in docs

* fixed trailing space

* add weight in tree model output

* fix bug

* updated Python plotting part to handle weights

* throw error when meet non ascii

* check ascii for config strings.

* add test

* fix a index bug

* Add parameter max_bin_by_feature.

* Fix minor bug.

* Fix minor bug.

* Fix calculation of header size for writing binary file.

* Fix style issues.

* Fix python style issue.

* Fix test and python style issue.

* add balanced bagging

* refine code

* fix format

* clarify usage only for binary application

* consider max_depth for histogram_pool_size

* Update serial_tree_learner.cpp

* Update config.cpp

* Update serial_tree_learner.cpp

* fix unsorted eval_at

* Update config.cpp

* Implement top-k multiclass error metric. Add new parameter top_k_threshold.

* Add test for multiclass metrics

* Make test less sensitive to avoid floating-point issues.

* Change tabs to spaces.

* Fix problem with test in Python 2. Refactor to use np.testing. Decrease number of training rounds so loss is larger and easier to compare.

* Move multiclass tests into test_engine.py

* Change parameter name from top_k_threshold to multi_error_top_k.

* Fix top-k error metric to handle case where scores are equal. Update tests and docs.

* Change name of top-k metric to multi_error@k.

* Change tabs to spaces.

* Fix formatting.

* Fix minor issues in docs.

* first metric only in earlystopping for cli

* code clean

* added note about CLI only usage

* removed note about CLI only usage

* fix a bug when bagging with reset_config

* clean code

* added all necessary includes - fixed build/include_what_you_use error

* fixed the order of includes (build/include_order)

* Add configuration parameters for CEGB.

* Add skeleton CEGB tree learner

Like the original CEGB version, this inherits from SerialTreeLearner.
Currently, it changes nothing from the original.

* Track features used in CEGB tree learner.

* Pull CEGB tradeoff and coupled feature penalty from config.

* Implement finding best splits for CEGB

This is heavily based on the serial version, but just adds using the coupled penalties.

* Set proper defaults for cegb parameters.

* Ensure sanity checks don't switch off CEGB.

* Implement per-data-point feature penalties in CEGB.

* Implement split penalty and remove unused parameters.

* Merge changes from CEGB tree learner into serial tree learner

* Represent features_used_in_data by a bitset, to reduce the memory overhead of CEGB, and add sanity checks for the lengths of the penalty vectors.

* Fix bug where CEGB would incorrectly penalise a previously used feature

The tree learner did not update the gains of previously computed leaf splits when splitting a leaf elsewhere in the tree.
This caused it to prefer new features due to incorrectly penalising splitting on previously used features.

* Document CEGB parameters and add them to the appropriate section.

* Remove leftover reference to cegb tree learner.

* Remove outdated diff.

* Fix warnings

* Fix minor issues identified by @StrikerRUS.

* Add docs section on CEGB, including citation.

* Fix link.

* Fix CI failure.

* Add some unit tests

* Fix pylint issues.

* Fix remaining pylint issue

* Fix index out-of-range exception generated by BaggingHelper on small datasets.

Prior to this change, the line "score_t threshold = tmp_gradients[top_k - 1];" would generate an exception, since tmp_gradients would be empty when the cnt input value to the function is zero.

* Update goss.hpp

* Update goss.hpp

* Add API method LGBM_BoosterPredictForMats which runs prediction on a data set given as of array of pointers to rows (as opposed to existing method LGBM_BoosterPredictForMat which requires data given as contiguous array)

* Fix incorrect upstream merge

* Add link to LightGBM.NET

* Fix indenting to 2 spaces

* Dummy edit to trigger CI

* Dummy edit to trigger CI