# Confidence score over xgboost logistic regression

The probabilities of logistic regression indicate how the certain the model is over predictions. if its 0.93 it means the model is 93% confident the label is 1 and 7% to be 0. or if the probability is 0.14 it means, 14% to be 1 and 86% to be zero.

In my case, I have 80% accuracy on the hold out data using 0.5 threshold over prediction. this value is calculated using AUC curve. However, if I set the threshold to be 0.9 to calculate the accuracy, the accuracy becomes 60%. Which is not so intuitive. I guess the probability over logistic regression are not necessarily the confidence or fitness of the model. I wonder if it is possible to get a confidence score over these values.

I have seen similar questions, where people have asked how to get confidence interval over prediction. This question is different, because the focus is on logistic regression.