We examine the efficiency of a competing classifier through sensitivity and specificity, utilizing a Monte Carlo Study.
We observed that when sensitivity or specificity or both are low, the efficiency of such classifier is poor and not desirable.
We found that even with large sample size empirical efficiency does not show any appreciable difference. Our results
suggest that estimation of efficiency is not good when we have small sample sizes (? 30 ). We found that if the sensitivity
or specificity or both are high (? 0.75 ), such classifier have good efficiency. This is slightly more relaxed than the results
by other researchers where sensitivity and specificity of .80 or higher was recommended.
