74db9757b3
* [R] MSVC compatibility * [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t * R package build with cmake and CUDA * R package CUDA build fixes and cleanups * always export the R package native initialization routine on windows * update the install instructions doc * fix lint * use static_cast directly to set BernoulliRng seed * [R] demo for GPU accelerated algorithm * tidy up the R package cmake stuff * R pack cmake: installs main dependency packages if needed * [R] version bump in DESCRIPTION * update NEWS * added short missing/sparse values explanations to FAQ
73 lines
3.4 KiB
Markdown
73 lines
3.4 KiB
Markdown
Frequently Asked Questions
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========================
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This document contains frequently asked questions about xgboost.
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How to tune parameters
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----------------------
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See [Parameter Tunning Guide](how_to/param_tuning.md)
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Description on the model
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------------------------
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See [Introduction to Boosted Trees](model.md)
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I have a big dataset
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--------------------
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XGBoost is designed to be memory efficient. Usually it can handle problems as long as the data fit into your memory
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(This usually means millions of instances).
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If you are running out of memory, checkout [external memory version](how_to/external_memory.md) or
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[distributed version](../demo/distributed-training) of xgboost.
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Running xgboost on Platform X (Hadoop/Yarn, Mesos)
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--------------------------------------------------
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The distributed version of XGBoost is designed to be portable to various environment.
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Distributed XGBoost can be ported to any platform that supports [rabit](https://github.com/dmlc/rabit).
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You can directly run xgboost on Yarn. In theory Mesos and other resource allocation engines can be easily supported as well.
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Why not implement distributed xgboost on top of X (Spark, Hadoop)
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-----------------------------------------------------------------
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The first fact we need to know is going distributed does not necessarily solve all the problems.
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Instead, it creates more problems such as more communication overhead and fault tolerance.
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The ultimate question will still come back to how to push the limit of each computation node
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and use less resources to complete the task (thus with less communication and chance of failure).
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To achieve these, we decide to reuse the optimizations in the single node xgboost and build distributed version on top of it.
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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).
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Such design allows us to reuse most of the code, while being portable to major platforms such as Hadoop/Yarn, MPI, SGE.
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Most importantly, it pushes the limit of the computation resources we can use.
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How can I port the model to my own system
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The model and data format of XGBoost is exchangeable,
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which means the model trained by one language can be loaded in another.
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This means you can train the model using R, while running prediction using
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Java or C++, which are more common in production systems.
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You can also train the model using distributed versions,
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and load them in from Python to do some interactive analysis.
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Do you support LambdaMART
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Yes, xgboost implements LambdaMART. Checkout the objective section in [parameters](parameter.md)
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How to deal with Missing Value
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xgboost supports missing value by default.
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In tree algorithms, branch directions for missing values are learned during training.
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Note that the gblinear booster treats missing values as zeros.
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Slightly different result between runs
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This could happen, due to non-determinism in floating point summation order and multi-threading.
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Though the general accuracy will usually remain the same.
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Why do I see different results with sparse and dense data?
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--------------------------------------------------------
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"Sparse" elements are treated as if they were "missing" by the tree booster, and as zeros by the linear booster.
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For tree models, it is important to use consistent data formats during training and scoring. |