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doc/faq.md

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+Frequent Asked Questions
+========================
+This document contains the frequent asked question to xgboost.
+
+How to tune parameters
+----------------------
+See [Parameter Tunning Guide](param_tuning.md)
+
+
+I have a big dataset
+--------------------
+XGBoost is designed to be memory efficient. Usually it could 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 [external memory version](external_memory.md) or
+[distributed version](https://github.com/dmlc/wormhole/tree/master/learn/xgboost) 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 engine 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 over head and fault tolerance.
+The ultimate question will still come back into 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 a 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, and being portable to major platforms such as Hadoop/Yarn, MPI, SGE.
+Most importantly, pushs 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 exchangable.
+Which means the model trained by one langauge 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 system.
+You can also train the model using distributed version,
+and load them in from python to do some interactive analysis.
+
+
+Do you support LambdaMART
+-------------------------
+Yes, xgboost implements LambdaMART. Checkout the objective section in [parameters](parameter.md)
+
+
+How to deal with Missing Value
+------------------------------
+xgboost support missing value by default
+
+
+Slightly different result between runs
+--------------------------------------
+This could happen, due to non-determinism in floating point summation order and multi-threading.
+Though the general accurac will usually remain the same.