uplift: Evaluate uplift for different (binary) classification models

In radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Evaluate uplift for different (binary) classification models

Description

Evaluate uplift for different (binary) classification models

Usage

uplift(
  dataset,
  pred,
  rvar,
  lev = "",
  tvar,
  tlev = "",
  qnt = 10,
  cost = 1,
  margin = 2,
  scale = 1,
  train = "All",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset	Dataset
pred	Predictions or predictors
rvar	Response variable
lev	The level in the response variable defined as success
tvar	Treatment variable
tlev	The level in the treatment variable defined as the treatment
qnt	Number of bins to create
cost	Cost for each connection (e.g., email or mailing)
margin	Margin on each customer purchase
scale	Scaling factor to apply to calculations
train	Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalbin
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from

Details

Evaluate uplift for different (binary) classification models based on predictions. See https://radiant-rstats.github.io/docs/model/evalbin.html for an example in Radiant

Value

A list of results

See Also

summary.evalbin to summarize results

plot.evalbin to plot results

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  str()

radiant.model documentation built on Oct. 16, 2023, 9:06 a.m.