GitLab

* delegated not showing hidden items to the rtd_theme

* refactored docs

* fixed docs

* reworker predict method of sklearn wrapper

* fixed encapsulation

* added test

* fixed consistency between docstring and params docs

* fixed verbose

* replaced predict_proba with predict in test

* fixed verbose again

* fixed fraction params descriptions

* added description of skip_drop and drop_rate constraints

* fixed subsample_freq consistency with C++ default value

* fixed nice look of params list

* made force splits json file example clickable

* fixed nice look of metrics list and added comma

* reduced warning in test about same param specified twice

* replaced pred_parameter with **kwargs in predict method

* added test for **kwargs in predict method

* fixed warnings

* fixed pylint

* add info on adaptive learning rate in the sklearn API

* adjust learning rate documentation following the PR discussion

* fix early stopping documentation

* improve wording

* fixing trailing spaces

* update sklearn fit parameter description

* update mtric parameter description

* first draft

* refine a branching

* fixed PyPI badge

* Update Parameters.rst

* Update Parameters.rst

* Update Parameters.rst

* fix broken url

* Update Features.rst

* added info about SWIG Java wrapper

* hotfix

replace the typo “reed" by "read".

* fix the name of custom objective function

* fix multi-class alias

* Update GPU-Windows.rst

* fixed typo

* fixed render of default value of metric param

* fixed typos

* added new aliases for regression_l2 to docs

* fixed new line

* fixed tweedie_variance_power description

* moved gamma and tweedie metrics to other regression metrics

* added new aliases for l2 to docs

* added new alias for l1 to docs

* added new alias for log loss to docs

* added new aliases for multiclass to docs

* added description for default value of metric param

* fixed link to poisson regression and capitalized regressions' names

* removed unused earlier alias type for gamma deviance

* added aliases for multiclass and multiclass_ova

* updated Quick-Start.rst according to Parameters.rst

* added cross-entropy into Features

* added aliases

fix the error in the number of experiments

Better description for setting OpenCL platform and GPU IDs in documentations

* unfinished

* some refines.

* fix a bug

* some refines

* remove useless files

* some small fixes

* clean code

* clean code

* fix connect

* fix a broken link

* fix a broken link

* Minor fix

* fixed table

* add output_freq to aliases

* reordered params set according to review comment

[python] added option to install python-package with mpi version and possibility to pass CMake options (#1034)

* fixed hierarchy of installation guide sections; made installation with proto support more verbose

* added option to install with protobuf support to python package

* fixed pylint

* added option to install MPI version to python package

* added tests for python-package installation with options --proto and --mpi

* hotfix

* removed unnecessary note from docs

* removed unnecessary line from travis script

* reverted proto

* Revert "reverted proto"

This reverts commit 5203448bb90a13844cf330527ca971eabb45a4a8.

* reverted protobuf from python-package

* added options for cmake in python-package

* fixed typos

* fixed options readability

* reworked installation logging

* fixed docs according to review comments

* fixed pylint

* fixed space in log path

* add code for refit tree

* add implementation.

* update documents.

* clean code

* fix a type

* Add n_estimators as num_iteration alias

Scikit-Learn's ensemble methods use the term `n_estimators` for the number of
iterations of training models. To make it more accessible for newcomers who are
familiar with Scikit-Learn, it would help if the Parameters page mentioned
`n_estimators` and what parameter that maps to within LightGBM's API.

Addresses discussion brought up in #954

* Add n_estimators as num_iterations alias

Adds n_estimators as an alias for num_iterations in the CLI as well as Python
libs. Additionally bumps the default for n_estimators in the Sklearn API to 100
estimators.