GitLab

* added python version badge

* fixed typos

* fixed links

* readthedocs doesn't support links with anchor out of box

* fixed table rendering at ReadTheDocs #776#issuecomment-319851551

* fixed table rendering at ReadTheDocs

* added link to Key-Events page

* fixed links

* hotfix

* fixed markdown

* added test for sklearn handle categorical features

* use raw X, y in sklearn wrapper in case of pandas.DataFrame

* fixed probs

* Fix feature attributions for regression models and add Python bindings

* Address pylint issue

* Lazy fix missing tree depth info

* travis cleanup

* removed precompiled files in windows folder from sdist command

* removed rubbish from install folder

* added compute folder

* disabled logs from compilers; fixed #874

* fixed safe clear_fplder

* added windows folder to manifest.in

* added windows folder to build

* added library path

* added compilation with MSBuild from .sln-file

* fixed unknown PlatformToolset returns exitcode 0

* hotfix

* updated Readme

* removed return

* added installation with mingw test to appveyor

* let's test appveyor with both VS 2015 and VS 2017; but MinGW isn't installed on VS 2017 image

* fixed built-in name 'file'

* simplified appveyor

* removed excess data_files

* fixed unreadable paths

* separated exceptions for cmake and mingw

* refactored silent_call

* don't create artifacts with VS 2015 and mingw

* be more precise with python versioning in Travis

* removed unnecessary if statement

* added classifiers for PyPI and python versions badge

* changed python version in travis

* added support of scikit-learn 0.18.x

* added more python versions to Travis

* added more python versions to Appveyor

* reduced number of tests in Travis

* Travis trick is not needed anymore

* attempt to fix according to https://github.com/Microsoft/LightGBM/pull/880#discussion_r137438856

* fixed description of Dataset

* fixed description of Booster

* fixed description of train

* fixed description of cv

* fixed description of callbacks

* fixed description of plotting

* hotfixes

This commit will regain backward compatibility that has been broken
after 015c8fff.

* 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

* fixed capital letters and formating

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

* 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

* 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

* 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.

* python-package: fix version

* remove version variable

* check params

* add test case

* fix pylint

* Fix Invalid decision type error while plotting tree in Python

* Fix Invalid decision type error while plotting tree in Python (modified = to ==)

* params back up

* refine logic

* refine python codes

* fix appveryor test

* add note to feature_importances

* added get_ref_chain function to Dataset object, used it in train() to compare if training and validation have common reference ancestors

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* removed check for handle = None

missing parameter "self" in signature of Booster.get_leaf_output

* add keep_training_booster

* use model string

* reset handle; free dataset

thanks for building in early_stopping! super useful.