# xgboost v0.4-1

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by Tong He

Extreme Gradient Boosting, which is an efficient implementation of gradient boosting framework. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. 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 predict,xgb.Booster-method Predict method for eXtreme Gradient Boosting model predict,xgb.Booster.handle-method Predict method for eXtreme Gradient Boosting model handle xgb.plot.tree Plot a boosted tree model xgb.DMatrix Contruct xgb.DMatrix object slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object xgb.load Load xgboost model from binary file xgb.cv Cross Validation nrow,xgb.DMatrix-method Number of xgb.DMatrix rows agaricus.train Training part from Mushroom Data Set xgb.train eXtreme Gradient Boosting Training setinfo Set information of an xgb.DMatrix object getinfo Get information of an xgb.DMatrix object xgb.save Save xgboost model to binary file agaricus.test Test part from Mushroom Data Set xgb.importance Show importance of features in a model xgboost eXtreme Gradient Boosting (Tree) library xgb.plot.importance Plot feature importance bar graph xgb.save.raw Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector xgb.model.dt.tree Convert tree model dump to data.table xgb.DMatrix.save Save xgb.DMatrix object to binary file xgb.dump Save xgboost model to text file No Results!