Description Usage Arguments Value Author(s) References Examples
generate random data for classification as in Long and Servedio (2010)
1 | dataLS(ntr, ntu = ntr, nte, percon)
|
ntr |
number of training data |
ntu |
number of tuning data, default is the same as |
nte |
number of test data |
percon |
proportion of contamination, must between 0 and 1. If |
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.
Zhu Wang
Maintainer: Zhu Wang zhuwang@gmail.com
P. Long and R. Servedio (2010), Random classification noise defeats all convex potential boosters, Machine Learning Journal, 78(3), 287–304.
1 | dat <- dataLS(ntr=100, nte=100, percon=0)
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