It actually depends on your specific application.
Say you want to minimize number of false positives (because it will cost you a lot to deal with consequences of any false alarm)
In this case, choose the first classifier, because its false positive rate is less then that of the second classifier:
58/(58+124) < 61/(61+127)
0.3186813 < 0.3244681
Take a look here
http://en.wikipedia.org/wiki/Accuracy_and_precision
and here:
http://en.wikipedia.org/wiki/Sensitivity_and_specificity
If you just want "the best classifier" - you have a problem, since both classifiers have the same accuracy:
a1 = (349+124)/(349+124+58+93) = 0.7580128
a2 = (346+127)/(346+127+61+90) = 0.7580128
So you need to analyze your domain or industry and decide whether you want to:
1) get as little false alarms as possible - then choose classifier with minimum false positive rate;
2) get as little missed cases as possible - then choose classifier with minimum false negative rate;
3) get more hits as possible - then choose classifier with maximum true positive rate;
4) get more correct rejections - then choose classifier with maximum true negative rate.
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