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* expose FeatureNumBin in C api

* parametrize min_data_in_bin and add test with max_bin_by_feature

* include feature_num_bin in R package

* add suggestion from review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update error message and lint

* lint

* add call method

* minor improvements in tests

* add suggestions from review

* lint

* rename argument to feature in python and r packages
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* better logging of column datatypes

* update to checking function

* fix typo

* Update python-package/lightgbm/basic.py
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Update python-package/lightgbm/basic.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [ci] update CODEOWNERS

* remove chivee from CODEOWNERS

[python-package] [R-package] propagate the best iteration of cvbooster into the individual boosters (#5066)

* Update test_windows.ps1

* Update .appveyor.yml

* Update test_windows.ps1

* Update .appveyor.yml

* fix duplicate added initial scores for single-leaf trees

* add test case

* Fix import in Python test

* commit python suggestions
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* docs: booster eval methods accept list of callables

* docs: remove extra space

[tests][python] move tests that use `train()` function defined in `engine.py` from `test_basic.py` to `test_engine.py` (#5034)

* Update test_basic.py

* Update test_engine.py

* Update test_engine.py

[python] fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective and eval (#5030)

* fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective

* Apply suggestions from code review
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Correct documentation for sparse predictions

The documentation says that the parameter `nindptr` for `LGBM_BoosterPredictSparseOutput` should be the number of rows plus one, but this is incorrect when the input type is CSC. This PR fixes it.

* Update c_api.h

* Update c_api.h

* Update c_api.h

* map nullable dtypes to regular float dtypes

* cast x3 to float after introducing missing values

* add test for regular dtypes

* use .astype and then values. update nullable_dtypes test and include test for regular numpy dtypes

* more specific allowed dtypes. test no copy when single float dtype df

* use np.find_common_type. set np.float128 to None when it isn't supported

* set default as type(None)

* move tests that use lgb.train to test_engine

* include np.float32 when finding common dtype

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* add linebreak
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update lightgbm.R

* Update lightgbm.Rd

* promote objective and init_score to top-level arguments

* follow comments

* Update R-package/R/lightgbm.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* update docs

* linter

* comments

* comments

* comments

* extend test for default objective

* Update R-package/tests/testthat/test_basic.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* docs: weight parameter non-negative

* docs: weights non negative only for train data

* docs: weights should be non negative for validation data

* typo in html render

* docs: brief weights non-negative description

[python-package] use 2d collections for predictions, grads and hess in multiclass custom objective (#4925)

* reshape predictions, grad and hess in multiclass custom objective

* add sklearn test. move custom obj to utils. docs for numpy

* use num_model_per_iteration to get num_classes

* update docs and dask multiclass custom objective test

* move reshaping to __inner_predict. add test for feval

* add missing note. remove extra line

* improve docs for sklearn wrapper

* empty commit

* install add scikit-learn to conda environment for building docs

* factor out lgb.check.obj()

* remove lgb.check.obj()

* add test on lgb.cv()

* clarify that categoricals will be converted to ints and not that they should be ints in the input data

* update remaining sections

* update config.h

* add suggestions

* CUDATreeLearner: free GPU memory in destruuctor if any allocated

* Minor changes: checking for num_gpu_feature_groups is not needed

* Trigger CI again

* [ci] [docs] use mamba for readthedocs builds (fixes #4954)

* update docs

* simplify build script and add docs flag to gitignore

* exit with non-0 if build fails

* update CI job

* add doxygen

* remove outdated requirement_base.txt reference

* use conda create instead of conda env create

* fix conda create flags

* add nodefaults to env.yml

* Update docs/README.rst
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* try to fix check-docs CI job

* additional changes

* switch from mamba to miniforge

* simplify docker command and fix issues in local build script

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update docs and conda

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update .appveyor.yml

* Update .vsts-ci.yml

* [R-package] prefer params to keyword argument in lgb.train()

* make test stricter

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update setup.py

* Update cuda.yml

* Update static_analysis.yml

* Update linkchecker.yml

* Update .appveyor.yml

* Update .vsts-ci.yml

* Update python_package.yml

* Update .vsts-ci.yml

* Update python_package.yml

* Update cuda.yml

* Update static_analysis.yml

* Update linkchecker.yml

* Update .vsts-ci.yml

* Update .appveyor.yml

* re-arange Python version

* do not produce model files on disk by default

* remove 'save_name' argument from 'lightgbm()'

* make record_evaluation compatible with cv

* test multiple metrics in cv

* lint

* fix cv with train metric. save stdv as well

* always add dataset prefix to cv_agg

* remove unused function

* update deps

* pin eigen

* add hidden bazel files to `.Rbuildignore`

* more verbose test logs

* more verbosity in logs for other tasks

* empty commit

* empty commit

* empty commit

* empty commit

* empty commit

* empty commit

* revert fast_double_parser update

* empty commit

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* revert tests verbosity

* test: swap compilers

* empty commit 1

* empty commit 2

* empty commit 3

* empty commit 4

* Revert "test: swap compilers"

This reverts commit e77b0013e9725cc82389f76f004e0367ed9295d8.

* Update test_dask.py

* Update test_engine.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_engine.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py