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In the case of balanced data balanced accuracy is the same as classification accuracy.
balanced accuracy.
Corresponding Balanced Accuracy values of the developed models varied in the range from 0.85 to 0.93.
BAC = balanced accuracy, SEN = sensitivity, SPEC = specificity, PPV = positive predictivity, NPV = negative predictivity.
Balanced accuracy (BA) is the arithmetic mean of sensitivity and specificity and represents a trade-off between the two values.
In Figure 7 the Balanced Accuracy is given as a function of data fraction after the exclusion of outliers.
Balanced Accuracy of the classifiers also turned out to be as good as was accuracy alone (Figure 2).
Balanced accuracy may be a better alternative in unbalanced data sets [43].
Balanced accuracy is the average between sensitivity and specificity.
Balanced Accuracy (BAC) avoids magnifying performance estimates of imbalanced datasets.
The use of both classification balanced accuracy and prediction balanced accuracy within the GEDT algorithm emphasizes generalizability of the final model.
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