uplift: 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()
)

Value

A list of results

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

Examples

Run this code

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