if (FALSE) {
library(h2o)
h2o.init()
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv"
train <- h2o.importFile(f)
train$treatment <- as.factor(train$treatment)
train$conversion <- as.factor(train$conversion)
model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion",
ntrees=10, max_depth=5, treatment_column="treatment",
auuc_type="AUTO")
perf <- h2o.performance(model, train=TRUE)
h2o.aecu(perf)
}
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