[docs] Add FLAML for efficient hyperparameter optimization (#4013)

* add FLAML for HPO in DOC

* add FLAML for HPO

* revise FLAML phasing

* Update docs/Parameters-Tuning.rst

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

* Update README.md

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

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

README.md

@@ -42,7 +42,8 @@ Next you may want to read:
 - [**Parameters**](https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.
 - [**Distributed Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
 - [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters.
-- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna/blob/master/examples/)).
+- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/) provides automated tuning for LightGBM ([code examples](https://github.com/microsoft/FLAML/blob/main/notebook/flaml_lightgbm.ipynb)).
+- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna/tree/master/examples/lightgbm)).
 
 Documentation for contributors:
 
@@ -59,6 +60,8 @@ Some old update logs are available at [Key Events](https://github.com/microsoft/
 External (Unofficial) Repositories
 ----------------------------------
 
+FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
+
 Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna
 
 Julia-package: https://github.com/IQVIA-ML/LightGBM.jl

docs/Parameters-Tuning.rst

@@ -7,6 +7,7 @@ This page contains parameters tuning guides for different scenarios.
 
 -  `Parameters <./Parameters.rst>`__
 -  `Python API <./Python-API.rst>`__
+-  `FLAML`_ for automated hyperparameter tuning
 -  `Optuna`_ for automated hyperparameter tuning
 
 Tune Parameters for the Leaf-wise (Best-first) Tree
@@ -214,3 +215,5 @@ Deal with Over-fitting
 -  Try increasing ``path_smooth``
 
 .. _Optuna: https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258
+
+.. _FLAML: https://github.com/microsoft/FLAML