generate_data: Generate the data.

In npcs: Neyman-Pearson Classification via Cost-Sensitive Learning

Generate the data.

Description

Generate the data from two simulation cases in Tian, Y., & Feng, Y. (2021).

Usage

generate_data(n = 1000, model.no = 1)

Arguments

n	the generated sample size. Default = 1000.
model.no	the model number in Tian, Y., & Feng, Y. (2021). Can be 1 or 2. Default = 1.

Value

A list with two components x and y. x is the predictor matrix and y is the label vector.

References

Tian, Y., & Feng, Y. (2021). Neyman-Pearson Multi-class Classification via Cost-sensitive Learning. Submitted. Available soon on arXiv.

See Also

npcs, predict.npcs, error_rate, and gamma_smote.

Examples

set.seed(123, kind = "L'Ecuyer-CMRG")
train.set <- generate_data(n = 1000, model.no = 1)
x <- train.set$x
y <- train.set$y

npcs documentation built on April 27, 2023, 9:10 a.m.