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Here we can see that the TP value would be 8 and the FP value would be 2 (the underlined numbers in the table). When we plug these numbers in the equation we are able to calculate the estimate: <math>E_p = TP - FP = 8 - 2 = 6</math>. This means that using the estimate on this feature would have it receive a score of 6.
However, it should be worth noting that this number is only an estimate. For example, if two features both had a FP value of 2 while one of the features had a higher TP value, that feature would be ranked higher than the other because the resulting estimate when using the equation would give a higher value. This could lead to some inaccuracies when using the metric if some features have more positive samples than others. To combat this, one could use a more powerful metric known as [[Sensitivity and specificity|Sensitivity]] that takes into account the proportions of the values from the confusion matrix to give the actual [[Sensitivity and specificity|true positive rate]] (TPR). The difference between these metrics is shown in the example below:
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