h2o.aecu: Retrieve the default AECU (Average Excess Cumulative Uplift =...

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Retrieve the default AECU (Average Excess Cumulative Uplift = area between AUUC and random AUUC)

Description

Retrieves the AECU value from an H2OBinomialUpliftMetrics. You need to specificy the type of AECU using metric parameter. Defaults "qini". Qini AECU equals the Qini value. If "train" and "valid" parameters are FALSE (default), then the training AECU value is returned. If more than one parameter is set to TRUE, then a named vector of AECUs are returned, where the names are "train", "valid".

Usage

h2o.aecu(object, train = FALSE, valid = FALSE, metric = "qini")

Arguments

object	An H2OBinomialUpliftMetrics
train	Retrieve the training AECU
valid	Retrieve the validation AECU
metric	Specify metric of AECU. Posibilities are "qini", "lift", "gain", defaults "qini".

Examples

## Not run: 
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)

## End(Not run)

h2o documentation built on Aug. 9, 2023, 9:06 a.m.