GitLab

* improved sklearn interface; added sklearns' tests

* moved best_score into the if statement

* improved docstrings; simplified LGBMCheckConsistentLength

* fixed typo

* pylint

* updated example

* fixed Ranker interface

* added missed boosting_type

* fixed more comfortable autocomplete without unused objects

* removed check for None of eval_at

* fixed according to review

* fixed typo

* added description of fit return type

* dictionary->dict for short

* markdown cleanup

* Add efficient demo for LightGBM

* Add bank dataset and more examples

* Add bank manual

Add Depth Column
Refine Code Structure

* added license badge

* added link to PyPI

* fixed capital letters and formating

* fixed CMake capital letters; added VS 2013 to the list of allowed versions

* Attempt to address issue #807 Poisson regression objective.

* Fix coding style.

* Added label non-negative safety check.

* expose feature importance to c_api

* support type=gain

* remove dump model from examples and tests temporarily because it's unstable

* use double instead of float

* Reorganize documentation references

* Fix R-package link name

* updated scikit-learn interface

* fixed better description

* updated set_params()

* removed backward compatibility

* removed excess lines

* replaced pop with setdefault

* added deprecated warnings

* added tests

* fixed links while viewing from wiki page

* fixed typos

* fixed repeated word and added markdown in FAQ.md

* fixed link to VS

* try to fix python memory leak

* fix parameter error.

* free old handle in _load_model_from_string .

* fix best_score & best_iteration

* refine logics.

* fix tests.

* output warning when only set `max_depth`

* Remove check of max_depth

max_depth can take both positive and negative value

* python-package: fix version

* remove version variable

* Prepare merging parameters

* Add parameters from params for lgb.train

* Usage of params in lgb.cv for rounds/early stop

* Double bracket (1)

* Double brackets (2)

* Explain individual predictions using SHAP value feature attributions

* Address code review

* check params

* add test case

* fix pylint