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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)
param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
watchlist <- list(eval = dtest, train = dtrain)
nrounds <- 2
# training the model for two rounds
bst <- xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
cat('start testing prediction from first n trees\n')
labels <- getinfo(dtest, 'label')
### predict using first 1 tree
ypred1 <- predict(bst, dtest, ntreelimit = 1)
# by default, we predict using all the trees
ypred2 <- predict(bst, dtest)
cat('error of ypred1=', mean(as.numeric(ypred1 > 0.5) != labels), '\n')
cat('error of ypred2=', mean(as.numeric(ypred2 > 0.5) != labels), '\n')
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