# xgboost v0.3-3

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by Tong He

Xgboost is short for eXtreme Gradient Boosting, which is an efficient and scalable implementation of gradient boosting framework. This package is an R wrapper of xgboost. The package includes efficient linear model solver and tree learning algorithms. 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. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

## Functions in xgboost

 Name Description getinfo Get information of an xgb.DMatrix object xgb.save.raw Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector agaricus.train Training part from Mushroom Data Set xgb.dump Save xgboost model to text file slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object xgb.plot.importance Plot feature importance bar graph xgb.cv Cross Validation predict,xgb.Booster.handle-method Predict method for eXtreme Gradient Boosting model handle xgb.train eXtreme Gradient Boosting Training xgb.plot.tree Plot a boosted tree model xgb.load Load xgboost model from binary file xgb.model.dt.tree Convert tree model dump to data.table predict,xgb.Booster-method Predict method for eXtreme Gradient Boosting model setinfo Set information of an xgb.DMatrix object agaricus.test Test part from Mushroom Data Set xgb.DMatrix Contruct xgb.DMatrix object xgboost eXtreme Gradient Boosting (Tree) library xgb.save Save xgboost model to binary file xgb.DMatrix.save Save xgb.DMatrix object to binary file xgb.importance Show importance of features in a model No Results!