Loading required libraries

library(MASS)
library(scoringTools)
library(rpart)

Mean vectors and variances for each class

d <- 2
mu0 <- array(0, c(1, d))
mu1 <- array(1, c(1, d))
sigma0 <- diag(1, d, d)
sigma1 <- diag(1, d, d)

Data generation

m <- 10000
set.seed(21)
y <- rbinom(m, 1, 0.5)
data <- array(0, c(m, d + 1))

x <- array(0, c(m, d))
x[y == 0, ] <- mvrnorm(n = sum(y == 0), mu0, sigma0)
x[y == 1, ] <- mvrnorm(n = sum(y == 1), mu1, sigma1)
data <- as.matrix(cbind.data.frame(y = y, x = x))
rm(x)
rm(y)

train <- as.data.frame(data[1:(m / 2), ])
test <- as.data.frame(data[(m / 2 + 1):m, ])

train[, "y"] <- as.factor(train[, "y"])

Oracle

modele_complet_arbre <- rpart(y ~ ., data = train, method = "class")
modele_complet_reglog <- glm(y ~ ., data = train, family = binomial(link = "logit"))

Loop over cut-off values

list_gini_arbre <- list()
list_gini_reglog <- list()

for (i in seq(0.2, 0.7, 0.05)) {
  ind_refuses_arbre <- predict(modele_complet_arbre, train)[, 1] < i
  ind_refuses_reglog <- predict(modele_complet_reglog, train, type = "response") < i

  train_partiel_arbre <- train[!ind_refuses_arbre, ]
  train_partiel_reglog <- train[!ind_refuses_reglog, ]

  modele_partiel_arbre <- rpart(y ~ ., data = train_partiel_arbre, method = "class")
  modele_partiel_reglog <- glm(y ~ ., data = train_partiel_reglog, family = binomial(link = "logit"))

  list_gini_arbre <- append(list_gini_arbre, normalizedGini(test[, "y"], predict(modele_partiel_arbre, test)[, 2]))
  list_gini_reglog <- append(list_gini_reglog, normalizedGini(test[, "y"], predict(modele_partiel_reglog, test, type = "response")))
}

Figures

plot(seq(0.2, 0.7, 0.05),
  list_gini_arbre,
  ylim = c(0.35, 0.7),
  xlab = "Rejection rate",
  ylab = "Gini",
  col = "red"
)
lines(seq(0.2, 0.7, 0.05), list_gini_reglog, ylim = c(0.35, 0.7), type = "p", col = "blue")

legend(1, 0.45,
  pch = c(1, 1), lty = c(1, 1),
  col = c("red", "blue"),
  legend = c("Decision tree", "Logistic regression"),
  cex = 1
)