# intuition behind classification model confidence intervals

What do confidence intervals mean in classification problems?

I recently did a study with glmnet in R, and got this confusion matrix :

Confusion Matrix and Statistics

Reference
Prediction  0  1
0 29  6
1  9 12

Accuracy : 0.7321
95% CI : (0.597, 0.8417)
No Information Rate : 0.6786
P-Value [Acc > NIR] : 0.2400

Kappa : 0.4118

Mcnemar's Test P-Value : 0.6056

Sensitivity : 0.7632
Specificity : 0.6667
Pos Pred Value : 0.8286
Neg Pred Value : 0.5714
Prevalence : 0.6786
Detection Rate : 0.5179
Detection Prevalence : 0.6250
Balanced Accuracy : 0.7149

'Positive' Class : 0


I understand in linear regression, that if the model predicts 10, with a confidence interval of +-2, then with 95% confidence the true value should be 10 +- 2.

But with binary classification, the solution is 0 or 1, or a probability of a data point being 0/1.

What does the confidence interval mean in this case? Does it mean, a prediction by the model is between 59.7 and 84.1% accurate 95% of the time?