About:
xgboost: eXtreme Gradient Boosting
It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily.
The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems
Changes:
Distributed version of xgboost that runs on YARN, scales to billions of examples
Direct save/load data and model from/to S3 and HDFS
Feature importance visualization in R module, by Michael Benesty
Predict leaf index
Poisson regression for counts data
Early stopping option in training
Native save load support in R and python
xgboost models now can be saved using save/load in R
xgboost python model is now pickable
sklearn wrapper is supported in python module
Experimental External memory version
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