[DOC] Update documentation

Author: xuyxu

README.rst

@@ -22,6 +22,8 @@ Deep Forest (DF) 21
 - **Efficient**: Fast training speed and high efficiency.
 - **Scalable**: Capable of handling large-scale data.
 
+Whenever one used tree-based machine learning approaches such as Random Forest or GBDT, DF21 may offer a new powerful option.
+
 For a quick start, please refer to `How to Get Started <http://www.lamda.nju.edu.cn/deep-forest/how_to_get_started.html>`__. For a detailed guidance on parameter tunning, please refer to `Parameters Tunning <http://www.lamda.nju.edu.cn/deep-forest/parameters_tunning.html>`__.
 
 Installation

docs/conf.py

@@ -18,7 +18,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'Deep Forest'
+project = 'Deep Forest (DF21)'
 copyright = '2021, LAMDA, Nanjing University, China'
 author = 'Yi-Xuan Xu'
 

docs/index.rst

@@ -8,7 +8,7 @@ DF21 Documentation
 - **Efficient**: Fast training speed and high efficiency.
 - **Scalable**: Capable of handling large-scale data.
 
-The package is actively being developed. The goal is to provide users from the industrial and academic community with **a third option on tree-based ensemble methods apart from Random Forest and Gradient Boosting Decision Tree**. To achieve this, any help would be welcomed. Please check the homepage on `Gitee <https://gitee.com/lamda-nju/deep-forest>`__ or `Github <https://github.com/LAMDA-NJU/Deep-Forest>`__ for details.
+Whenever one used tree-based machine learning approaches such as Random Forest or GBDT, DF21 may offer a new powerful option. This package is actively being developed, and any help would be welcomed. Please check the homepage on `Gitee <https://gitee.com/lamda-nju/deep-forest>`__ or `Github <https://github.com/LAMDA-NJU/Deep-Forest>`__ for details.
 
 Guidepost
 ---------