15 jan 2021 · This is an introductory document of using the xgboost package in R xgboost is short for eXtreme Gradient Boosting package It is an efficient
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[PDF] xgboost: eXtreme Gradient Boosting
15 jan 2021 · This is an introductory document of using the xgboost package in R xgboost is short for eXtreme Gradient Boosting package It is an efficient
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xgboost: eXtreme Gradient Boosting
Tianqi Chen, Tong He
Package Version: 1.7.5.1
March 31, 2023
1 Introduction
This is an introductory document of using thexgboostpackage in R. xgboostis short for eXtreme Gradient Boosting package. It is an eiÌifiÌicient and scalable implementation of gradient boosting framework by (Friedman, 2001) (Friedmanet al., 2000).The package includes eiÌifiÌicient linear model solver and tree learning algorithm. It supports various
objective functions, including regression, classiification and ranking. The package is made to be extendible, so that users are also allowed to deifine their own objectives easily. It has several features: 1. Sp eed:xgboostcan automatically do parallel computation on Windows and Linux, with openmp. It is generally over 10 times faster thangbm. 2. Input T ype:xgboosttakes several types of input data: ?Dense Matrix: R's dense matrix, i.e.matrix ?Sparse Matrix: R's sparse matrixMatrix::dgCMatrix ?Data File: Local data ifiles ?xgb.DMatrix:xgboost's own class. Recommended. 3. Sparsit y:xgboostaccepts sparse input for both tree booster and linear booster, and is optimized for sparse input. 4. Customization: xgboostsupports customized objective function and evaluation function 5. P erformance:xgboosthas better performance on several diffferent datasets.2 Example with Mushroom data
In this section, we will illustrate some common usage ofxgboost. The Mushroom data is cited from UCI Machine Learning Repository. (Bache and Lichman, 2013)library(xgboost) data (agaricus.train, package 'xgboost' data (agaricus.test, package 'xgboost' train agaricus.train test agaricus.test bst xgboost data = train data, label = train label, max_depth 2 eta 1 nrounds 2 objective "binary:logistic" ## [1] train-logloss:0.233376 ## [2] train-logloss:0.136658 xgb.save (bst, 'model.save' ## [1] TRUE bst xgb.load 'model.save' pred predict (bst, test data) xgboostis the main function to train aBooster, i.e. a model.predictdoes prediction on the model. Here we can save the model to a binary local ifile, and load it when needed. We can't inspectthe trees inside. However we have another function to save the model in plain text.xgb.dump(bst,'model.dump' )
## [1] TRUEThe output looks like
1 booster[0]:0:[f28<1.00001] yes=1,no=2,missing=2
1:[f108<1.00001] yes=3,no=4,missing=4
3:leaf=1.85965
4:leaf=-1.94071
2:[f55<1.00001] yes=5,no=6,missing=6
5:leaf=-1.70044
6:leaf=1.71218
booster[1]:0:[f59<1.00001] yes=1,no=2,missing=2
1:leaf=-6.23624
2:[f28<1.00001] yes=3,no=4,missing=4
3:leaf=-0.96853
4:leaf=0.784718
It is important to knowxgboost's own data type:xgb.DMatrix. It speeds upxgboost, and is needed for advanced features such as training from initial prediction value, weighted training instance.We can usexgb.DMatrixto construct anxgb.DMatrixobject:dtrain<- xgb.DMatrix (train$data,label = train $label)
class (dtrain) ## [1] "xgb.DMatrix" head getinfo (dtrain, 'label' ## [1] 1 0 0 1 0 0We can also save the matrix to a binary ifile. Then load it simply withxgb.DMatrixxgb.DMatrix.save(dtrain,'xgb.DMatrix' )
## [1] TRUE dtrain xgb.DMatrix 'xgb.DMatrix' ## [02:10:40] 6513x126 matrix with 143286 entries loaded from xgb.DMatrix