Frequently Asked Questions
This document contains frequently asked questions about XGBoost.
How to tune parameters
Description on the 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.
How to handle categorical feature?
Visit this tutorial for a walk through of categorical data handling and some worked examples.
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 the 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 APIs (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 a 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. See Model IO for more information.
Do you support LambdaMART?
Yes, XGBoost implements LambdaMART. Checkout the objective section in parameters.
How to deal with missing values
XGBoost supports missing values by default. In tree algorithms, branch directions for missing values are learned during training. Note that the gblinear booster treats missing values as zeros.
missing parameter is specified, values in the input predictor that is equal to
missing will be treated as missing and removed. By default it’s set to
Slightly different result between runs
This could happen, due to non-determinism in floating point summation order and multi-threading. Also, data partitioning changes by distributed framework can be an issue as well. 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. However, if we convert the sparse matrix back to dense matrix, the sparse matrix might fill the missing entries with 0, which is a valid value for xgboost.