add boost from prediction

R-package/NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2 (4.0.1): do not edit by hand
 
 export(getinfo)
+export(setinfo)
 export(slice)
 export(xgb.DMatrix)
 export(xgb.DMatrix.save)

R-package/demo/README.md

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+XGBoost R Feature Walkthrough
+====
+* [Basic walkthrough of wrappers](basic_walkthrough.R) 
+* [Cutomize loss function, and evaluation metric](custom_objective.R)
+* [Boosting from existing prediction](boost_from_prediction.R)
+* [Predicting using first n trees](predict_first_ntree.py)
+* [Generalized Linear Model](generalized_linear_model.py)
+* [Cross validation](cross_validation.py)

R-package/demo/boost_from_prediction.R

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+require(xgboost)
+# load in the agaricus dataset
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+
+watchlist <- list(eval = dtest, train = dtrain)
+###
+# advanced: start from a initial base prediction
+#
+print('start running example to start from a initial prediction')
+# train xgboost for 1 round
+param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
+bst <- xgb.train( param, dtrain, 1, watchlist )
+# Note: we need the margin value instead of transformed prediction in set_base_margin
+# do predict with output_margin=TRUE, will always give you margin values before logistic transformation
+ptrain <- predict(bst, dtrain, outputmargin=TRUE)
+ptest  <- predict(bst, dtest, outputmargin=TRUE)
+# set the base_margin property of dtrain and dtest
+# base margin is the base prediction we will boost from
+setinfo(dtrain, "base_margin", ptrain)
+setinfo(dtest, "base_margin", ptest)
+
+print('this is result of boost from initial prediction')
+bst <- xgb.train( param, dtrain, 1, watchlist )