Here something you can try if I've understood well:
# a model with a famous dataset model <- glm(formula= vs ~ wt + disp, data=mtcars, family=binomial) # let's predict the same data: use type response to have probability as result pred <- predict(model, mtcars, type="response") library(caret) # here you decide the cutoff pred_<- as.factor(ifelse(pred>0.77,'1','0')) # here we go! confusionMatrix(pred_, as.factor(mtcars$vs))
Confusion Matrix and Statistics Reference Prediction 0 1 0 18 3 1 0 11 Accuracy : 0.9062 95% CI : (0.7498, 0.9802) No Information Rate : 0.5625 P-Value [Acc > NIR] : 2.684e-05 Kappa : 0.8049 Mcnemar's Test P-Value : 0.2482 Sensitivity : 1.0000 Specificity : 0.7857 Pos Pred Value : 0.8571 Neg Pred Value : 1.0000 Prevalence : 0.5625 Detection Rate : 0.5625 Detection Prevalence : 0.6562 Balanced Accuracy : 0.8929 'Positive' Class : 0

Just out of curiosity I've taken a look at what happens under the hood, and I've used [dtruss/strace][1] on each test.
C++
./a.out < in Saw 6512403 lines in 8 seconds. Crunch speed: 814050
syscalls `sudo dtruss -c ./a.out < in`
CALL COUNT __mac_syscall 1 <snip> open 6 pread 8 mprotect 17 mmap 22 stat64 30 read_nocancel 25958

Python
./a.py < in Read 6512402 lines in 1 seconds. LPS: 6512402
syscalls `sudo dtruss -c ./a.py < in`
CALL COUNT __mac_syscall 1 <snip> open 5 pread 8 mprotect 17 mmap 21 stat64 29
[1]: http://en.wikipedia.org/wiki/Strace