Doc modernization (#3474)

* Change doc build to reST exclusively

* Rewrite Intro doc in reST; create toctree

* Update parameter and contribute

* Convert tutorials to reST

* Convert Python tutorials to reST

* Convert CLI and Julia docs to reST

* Enable markdown for R vignettes

* Done migrating to reST

* Add guzzle_sphinx_theme to requirements

* Add breathe to requirements

* Fix search bar

* Add link to user forum

doc/faq.rst

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+##########################
+Frequently Asked Questions
+##########################
+
+This document contains frequently asked questions about XGBoost.
+
+**********************
+How to tune parameters
+**********************
+See :doc:`Parameter Tunning Guide </tutorials/param_tuning>`.
+
+************************
+Description on the model
+************************
+See :doc:`Introduction to Boosted Trees </tutorials/model>`.
+
+********************
+I have a big dataset
+********************
+XGBoost is designed to be memory efficient. Usually it can handle problems as long as the data fit into your memory.
+(This usually means millions of instances)
+If you are running out of memory, checkout :doc:`external memory version </tutorials/external_memory>` or
+:doc:`distributed version </tutorials/aws_yarn>` of XGBoost.
+
+**************************************************
+Running XGBoost on Platform X (Hadoop/Yarn, Mesos)
+**************************************************
+The distributed version of XGBoost is designed to be portable to various environment.
+Distributed XGBoost can be ported to any platform that supports `rabit <https://github.com/dmlc/rabit>`_.
+You can directly run XGBoost on Yarn. In theory Mesos and other resource allocation engines can be easily supported as well.
+
+*****************************************************************
+Why not implement distributed XGBoost on top of X (Spark, Hadoop)
+*****************************************************************
+The first fact we need to know is going distributed does not necessarily solve all the problems.
+Instead, it creates more problems such as more communication overhead and fault tolerance.
+The ultimate question will still come back to how to push the limit of each computation node
+and use less resources to complete the task (thus with less communication and chance of failure).
+
+To achieve these, we decide to reuse the optimizations in the single node XGBoost and build distributed version on top of it.
+The demand of communication in machine learning is rather simple, in the sense that we can depend on a limited set of API (in our case rabit).
+Such design allows us to reuse most of the code, while being portable to major platforms such as Hadoop/Yarn, MPI, SGE.
+Most importantly, it pushes the limit of the computation resources we can use.
+
+*****************************************
+How can I port the model to my own system
+*****************************************
+The model and data format of XGBoost is exchangeable,
+which means the model trained by one language can be loaded in another.
+This means you can train the model using R, while running prediction using
+Java or C++, which are more common in production systems.
+You can also train the model using distributed versions,
+and load them in from Python to do some interactive analysis.
+
+*************************
+Do you support LambdaMART
+*************************
+Yes, XGBoost implements LambdaMART. Checkout the objective section in :doc:`parameters </parameter>`.
+
+******************************
+How to deal with Missing Value
+******************************
+XGBoost supports missing value by default.
+In tree algorithms, branch directions for missing values are learned during training.
+Note that the gblinear booster treats missing values as zeros.
+
+**************************************
+Slightly different result between runs
+**************************************
+This could happen, due to non-determinism in floating point summation order and multi-threading.
+Though the general accuracy will usually remain the same.
+
+**********************************************************
+Why do I see different results with sparse and dense data?
+**********************************************************
+"Sparse" elements are treated as if they were "missing" by the tree booster, and as zeros by the linear booster.
+For tree models, it is important to use consistent data formats during training and scoring.