Learn R Programming

irboost (version 0.1-1.5)

dataLS: generate random data for classification as in Long and Servedio (2010)

Description

generate random data for classification as in Long and Servedio (2010)

Usage

dataLS(ntr, ntu = ntr, nte, percon)

Value

a list with elements xtr, xtu, xte, ytr, ytu, yte for predictors of disjoint training, tuning and test data, and response variable -1/1 of training, tuning and test data.

Arguments

ntr

number of training data

ntu

number of tuning data, default is the same as ntr

nte

number of test data

percon

proportion of contamination, must between 0 and 1. If percon > 0, the labels of the corresponding percenrage of response variable in the training and tuning data are flipped.

Author

Zhu Wang
Maintainer: Zhu Wang zhuwang@gmail.com

References

P. Long and R. Servedio (2010), Random classification noise defeats all convex potential boosters, Machine Learning Journal, 78(3), 287--304.

Examples

Run this code
dat <- dataLS(ntr=100, nte=100, percon=0)

Run the code above in your browser using DataLab