radiant.model (version 0.8.0)

evalbin: Model evalbin

Description

Model evalbin

Usage

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

Arguments

dataset

Dataset name (string). This can be a dataframe in the global environment or an element in an r_data list from Radiant

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

method

Use either ntile or xtile to split the data (default is xtile)

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

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 {
result <- evalbin("titanic", c("age","fare"), "survived")

# }