# 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)
#  this script demonstrate how to fit generalized linear model in xgboost
#  basically, we are using linear model, instead of tree for our boosters
#  you can fit a linear regression, or logistic regression model

# change booster to gblinear, so that we are fitting a linear model
# alpha is the L1 regularizer
# lambda is the L2 regularizer
# you can also set lambda_bias which is L2 regularizer on the bias term
param <- list(objective = "binary:logistic", booster = "gblinear",
              nthread = 2, alpha = 0.0001, lambda = 1)

# normally, you do not need to set eta (step_size)
# XGBoost uses a parallel coordinate descent algorithm (shotgun),
# there could be affection on convergence with parallelization on certain cases
# setting eta to be smaller value, e.g 0.5 can make the optimization more stable

# the rest of settings are the same
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2
bst <- xgb.train(param, dtrain, num_round, watchlist)
ypred <- predict(bst, dtest)
labels <- getinfo(dtest, 'label')
cat('error of preds=', mean(as.numeric(ypred > 0.5) != labels), '\n')

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xgboost documentation built on April 16, 2022, 5:05 p.m.