Description Details Author(s) References Examples
This package provides the function to estimate model performance measures (L_1, L_2, C-statistics). The difference in the distribution of predictors between two datasets (training and validation) is adjusted by a density ratio estimate.
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Eisuke Inoue, Hajime Uno
Maintainer: Eisuke Inoue <eisuke.inoue@marianna-u.ac.jp>
Sugiyama, M., Suzuki, T. & Kanamori, T. Density Ratio Estimation in Machine Learning. Cambridge University Press 2012. ISBN:9781139035613.
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# generating learning data
n0 = 100
Z = cbind(rbeta(n0, 5, 5), rbeta(n0, 5, 5))
Y = apply(Z, 1, function (xx) {
rbinom(1, 1, (1/(1+exp(-(sum(c(-2,2,2) * c(1,xx)))))))})
dat = data.frame(Y=Y, Za=Z[,1], Zb=Z[,2])
# the model to be evaluated
mdl = glm(Y~., binomial, data=dat)
# validation dataset, with different centers on predictors
n1 = 100
Z1 = cbind(rbeta(n1, 6, 4), rbeta(n1, 6, 4))
Y1 = apply(Z1, 1, function (xx) {
rbinom(1, 1, (1/(1+exp(-(sum(c(-2,2,2) * c(1,xx)))))))})
dat1 = data.frame(Y=Y1, Za=Z1[,1], Zb=Z1[,2])
# calculation of L1 and L2 for this model
appe.glm(mdl, dat, dat1, reps=0)
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