# Gini above 1 when bootstrapping

Let's say I have a dataset (data), which contains the binary target variable class and the predictions (probabilities in [0,1]).

 data<-data.frame(class=c(1,1,1,0,0,0,1,0,1,1,0,1),predict=c(0.8,0.8,0.8,0.1,0.1,0.95,0.1,0.2,0.5,0.9,0.1,0.99))
metric <- function (D, d) {return (Hmisc::somers2(D$$predict[d], D$$class[d])[2])}
#metric(data)
boot.data <- boot::boot(data,metric, R=1000, sim="ordinary", stype="i")
ci <- boot::boot.ci(boot.data, conf=0.95, type="basic")
ci


The results are:

Intervals :
Level      Basic
95%   ( 0.0286,  1.2878 )
Calculations and Intervals on Original Scale


I would like to ask why I get values above 1 (1.2878) for the error bar of the Gini, although Gini maximum value should be 1. I assume that there may be some kind of theoritical distribution fitted, through which the confidence interval is produced.