radiant.model (version 0.9.7)

evalbin: Evaluate the performance of different (binary) classification models

Description

Evaluate the performance of different (binary) classification models

Usage

evalbin(dataset, pred, rvar, lev = "", qnt = 10, cost = 1,
  margin = 2, train = "All", data_filter = "")

Arguments

dataset

Dataset

pred

Predictions or predictors

rvar

Response variable

lev

The level in the response variable defined as success

qnt

Number of bins to create

cost

Cost for each connection (e.g., email or mailing)

margin

Margin on each customer purchase

train

Use data from training ("Training"), validation ("Validation"), 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")

Value

A list of results

Details

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

See Also

summary.evalbin to summarize results

plot.evalbin to plot results

Examples

# NOT RUN {
data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  str()

# }