How do i compute the bootstrapped mean? I computed 3 validation accuracies using 3 fold accuracies, and wanted to compute the overall mean, or a bootstrapped mean?
Lets say i have these accuracies for the 3 folds being 0.1, 0.5 and 0.3 .
I understand the concept of bootstrapping, meaning to reuse data you already have, but how do i "reuse" the same data to compute the mean?..
So I am currently training a DNN acoustic model for speech recognition, and to ensure performance or accuracy of the validation accuracy, I need some idea of the of the range of which the accuracies lies in.
Usually would one do 10 fold CV, but given the amount of data and the time it would take for train all folds, I decided to go to the other way, and compute 3 fold CV, and bootstrapping the resulting distribution (an so called "accuracy distribution") => thereby extracting the mean, variance, and CI..
But doing that doesn't seem to be that simple or?
I initially thought that doing 10 fold CV and 3 fold CV with bootstrapping would in some sense give the same result, but that does not seem to be case?